id stringlengths 8 19 | document stringlengths 2.18k 16.2k | challenge stringlengths 76 208 | approach stringlengths 79 223 | outcome stringlengths 84 209 |
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P06-1112 | In this paper , we explore correlation of dependency relation paths to rank candidate answers in answer extraction . Using the correlation measure , we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question . Different from previous studie... | A generally accessible NER system for QA systems produces a larger answer candidate set which would be hard for current surface word-level ranking methods. | They propose a statistical method which takes correlations of dependency relation paths computed by the Dynamic Time Wrapping algorithm into account for ranking candidate answers. | The proposed method outperforms state-of-the-art syntactic relation-based methods by up to 20% and shows it works even better on harder questions where NER performs poorly. |
2020.acl-main.528 | Recently , many works have tried to augment the performance of Chinese named entity recognition ( NER ) using word lexicons . As a representative , Lattice-LSTM ( Zhang and Yang , 2018 ) has achieved new benchmark results on several public Chinese NER datasets . However , Lattice-LSTM has a complex model architecture .... | Named entity recognition in Chinese requires word segmentation causes errors or character-level model with lexical features that is complex and expensive. | They propose to encode lexicon features into character representations so it can keep the system simpler and achieve faster inference than previous models. | The proposed efficient character-based LSTM method with lexical features achieves 6.15 times faster inference speed and better performance than previous models. |
P19-1352 | Word embedding is central to neural machine translation ( NMT ) , which has attracted intensive research interest in recent years . In NMT , the source embedding plays the role of the entrance while the target embedding acts as the terminal . These layers occupy most of the model parameters for representation learning ... | Word embeddings occupy a large amount of memory, and weight tying does not mitigate this issue for distant language pairs on translation tasks. | They propose a language independet method where a model shares embeddings between source and target only when words have some common characteristics. | Experiments on machine translation datasets involving multiple language families and scripts show that the proposed model outperforms baseline models while using fewer parameters. |
D12-1061 | This paper explores log-based query expansion ( QE ) models for Web search . Three lexicon models are proposed to bridge the lexical gap between Web documents and user queries . These models are trained on pairs of user queries and titles of clicked documents . Evaluations on a real world data set show that the lexicon... | Term mismatches between a query and documents hinder retrievals of relevant documents and black box statistical machine translation models are used to expand queries. | They propose to train lexicon query expansion models by using transaction logs that contain pairs of queries and titles of clicked documents. | The proposed query expansion model enables retrieval systems to significantly outperform models with previous expansion models while being more transparent. |
N07-1011 | Traditional noun phrase coreference resolution systems represent features only of pairs of noun phrases . In this paper , we propose a machine learning method that enables features over sets of noun phrases , resulting in a first-order probabilistic model for coreference . We outline a set of approximations that make t... | Existing approaches treat noun phrase coreference resolution as a set of independent binary classifications limiting the features to be only pairs of noun phrases. | They propose a machine learning method that uses sets of noun phrases as features that are coupled with a sampling method to enable scalability. | Evaluation on the ACE coreference dataset, the proposed method achieves a 45% error reduction over a previous method. |
2021.acl-long.67 | Bilingual lexicons map words in one language to their translations in another , and are typically induced by learning linear projections to align monolingual word embedding spaces . In this paper , we show it is possible to produce much higher quality lexicons with methods that combine ( 1 ) unsupervised bitext mining ... | Existing methods to induce bilingual lexicons use linear projections to align word embeddings that are based on unrealistic simplifying assumptions. | They propose to use both unsupervised bitext mining and unsupervised word alignment methods to produce higher quality lexicons. | The proposed method achieves the state-of-the-art in the bilingual lexical induction task while keeping the interpretability of their pipeline. |
D18-1065 | In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy , accurate , and efficient attention mechanism for sequence to sequence learning . The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention . O... | Softmax attention models are popular because of their differentiable and easy to implement nature while hard attention models outperform them when successfully trained. | They propose a method to approximate the joint attention-output distribution which provides sharp attention as hard attention and easy implementation as soft attention. | The proposed approach outperforms soft attention models and recent hard attention and Sparsemax models on five translation tasks and also on morphological inflection tasks. |
2022.acl-long.304 | Contrastive learning has achieved impressive success in generation tasks to militate the " exposure bias " problem and discriminatively exploit the different quality of references . Existing works mostly focus on contrastive learning on the instance-level without discriminating the contribution of each word , while key... | Existing works on contrastive learning for text generation focus only on instance-level while word-level information such as keywords is also of great importance. | They propose a CVAE-based hierarchical contrastive learning within instance and keyword-level using a keyword graph which iteratively polishes the keyword representations. | The proposed model outperforms CVAE and baselines on storytelling, paraphrasing, and dialogue generation tasks. |
2020.emnlp-main.384 | Word embedding models are typically able to capture the semantics of words via the distributional hypothesis , but fail to capture the numerical properties of numbers that appear in a text . This leads to problems with numerical reasoning involving tasks such as question answering . We propose a new methodology to assi... | Existing word embeddings treat numbers like words failing to capture numeration and magnitude properties of numbers which is problematic for tasks such as question answering. | They propose a deterministic technique to learn numerical embeddings where cosine similarity reflects the actual distance and a regularization approach for a contextual setting. | A Bi-LSTM network initialized with the proposed embedding shows the ability to capture numeration and magnitude and to perform list maximum, decoding, and addition. |
P12-1103 | We propose a novel approach to improve SMT via paraphrase rules which are automatically extracted from the bilingual training data . Without using extra paraphrase resources , we acquire the rules by comparing the source side of the parallel corpus with the target-to-source translations of the target side . Besides the... | Incorporating paraphrases improves statistical machine translation however no works investigate sentence level paraphrases. | They propose to use bilingual training data to obtain paraphrase rules on word, phrase and sentence levels to rewrite inputs to be MT-favored. | The acquired paraphrase rules improve translation qualities in oral and news domains. |
N09-1072 | Automatically extracting social meaning and intention from spoken dialogue is an important task for dialogue systems and social computing . We describe a system for detecting elements of interactional style : whether a speaker is awkward , friendly , or flirtatious . We create and use a new spoken corpus of 991 4-minut... | Methods to extract social meanings such as engagement from speech remain unknown while it is important in sociolinguistics and to develop socially aware computing systems. | They create a spoken corpus from conversations in speed-dating and perform analysis using extracted dialogue features with a focus on genders. | They found several gender dependent and independent phenomena in conversations related to the speed of speaking, laughing or asking questions. |
P18-1256 | We introduce the task of predicting adverbial presupposition triggers such as also and again . Solving such a task requires detecting recurring or similar events in the discourse context , and has applications in natural language generation tasks such as summarization and dialogue systems . We create two new datasets f... | Adverbaial triggers indicate the event recurrence, continuation, or termination in the discourse context and are frequently found in English but there are few related works. | They introduce an adverbial presupposition trigger prediction task and datasets and propose an attention mechanism that augments a recurrent neural network without additional trainable parameters. | The proposed model outperforms baselines including an LSTM-based language model on most of the triggers on the two datasets. |
P08-1116 | This paper proposes a novel method that exploits multiple resources to improve statistical machine translation ( SMT ) based paraphrasing . In detail , a phrasal paraphrase table and a feature function are derived from each resource , which are then combined in a log-linear SMT model for sentence-level paraphrase gener... | Paraphrase generation requires monolingual parallel corpora which is not easily obtainable, and few works focus on using the extracted phrasal paraphrases in sentence-level paraphrase generation. | They propose to exploit six paraphrase resources to extract phrasal paraphrase tables that are further used to build a log-linear statistical machine translation-based paraphrasing model. | They show that using multiple resources enhances paraphrase generation quality in precision on phrase and sentence level especially when they are similar to user queries. |
P08-1027 | There are many possible different semantic relationships between nominals . Classification of such relationships is an important and difficult task ( for example , the well known noun compound classification task is a special case of this problem ) . We propose a novel pattern clusters method for nominal relationship (... | Using annotated data or semantic resources such as WordNet for relation classification introduces errors and such data is not available in many domains and languages. | They propose an unsupervised pattern clustering method for nominal relation classification using a large generic corpus enabling scale in domain and language. | Experiments on the ACL SemEval-07 dataset show the proposed method performs better than existing methods that do not use disambiguation tags. |
2021.emnlp-main.185 | Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability . Conventional approaches employ the siamese-network for this task , which obtains the sentence embeddings through modeling the context-response semantic relevance by applying a feed-fo... | Existing methods to learn representations from dialogues have a similarity-measurement gap between training and evaluation time and do not exploit the multi-turn structure of data. | They propose a dialogue-based contrastive learning approach to learn sentence embeddings from dialogues by modelling semantic matching relationships between the context and response implicitly. | The proposed approach outperforms baseline methods on two newly introduced tasks coupled with three multi-turn dialogue datasets in terms of MAP and Spearman's correlation measures. |
P02-1051 | Named entity phrases are some of the most difficult phrases to translate because new phrases can appear from nowhere , and because many are domain specific , not to be found in bilingual dictionaries . We present a novel algorithm for translating named entity phrases using easily obtainable monolingual and bilingual re... | Translating named entities is challenging since they can appear from nowhere, and cannot be found in bilingual dictionaries because they are domain specific. | They propose an algorithm for Arabic-English named entity translation which uses easily obtainable monolingual and bilingual resources and a limited amount of hard-to-obtain bilingual resources. | The proposed algorithm is compared with human translators and a commercial system and it performs at near human translation. |
E06-1014 | Probabilistic Latent Semantic Analysis ( PLSA ) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis ( LSA ) . However , the parameters of a PLSA model are trained using the Expectation Maximization ( EM ) algorithm , and as a result , the trained model is d... | EM algorithm-baed Probabilistic latent semantic analysis models provide high variance in performance and models with different initializations are not comparable. | They propose to use Latent Semantic Analysis to initialize probabilistic latent semantic analysis models, EM algorithm is further used to refine the initial estimate. | They show that the model initialized in the proposed method always outperforms existing methods. |
2021.naacl-main.34 | We rely on arguments in our daily lives to deliver our opinions and base them on evidence , making them more convincing in turn . However , finding and formulating arguments can be challenging . In this work , we present the Arg-CTRL-a language model for argument generation that can be controlled to generate sentence-l... | Argumentative content generation can support humans but current models produce lengthy texts and offer a little controllability on aspects of the argument for users. | They train a controllable language model on a corpus annotated with control codes provided by a stance detection model and introduce a dataset for evaluation. | The proposed model can generate arguments that are genuine and argumentative and grammatically correct and also counter-arguments in a transparent and interpretable way. |
N16-1181 | We describe a question answering model that applies to both images and structured knowledge bases . The model uses natural language strings to automatically assemble neural networks from a collection of composable modules . Parameters for these modules are learned jointly with network-assembly parameters via reinforcem... | Existing works on visual learning use manually-specified modular structures. | They propose a question-answering model trained jointly to translate from questions to dynamically assembled neural networks then produce answers with using images or knowledge bases. | The proposed model achieves state-of-the-arts on visual and structured domain datasets showing that coutinous representations improve the expressiveness and learnability of semantic parsers. |
2020.aacl-main.88 | Large pre-trained language models reach stateof-the-art results on many different NLP tasks when fine-tuned individually ; They also come with a significant memory and computational requirements , calling for methods to reduce model sizes ( green AI ) . We propose a twostage model-compression method to reduce a model '... | Existing coarse-grained approaches for reducing the inference time of pretraining models remove layers, posing a trade-off between compression and the accuracy of a model. | They propose a model-compression method which decompresses the matrix and performs feature distillation on the internal representations to recover from the decomposition. | The proposed method reduces the model size by 0.4x and increases inference speed by 1.45x while keeping the performance degradation minimum on the GLUE benchmark. |
D16-1205 | Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words . Current DSMs , however , represent context words as separate features , thereby loosing important information for word expectations , such as word interrelations . In this paper , we present a D... | Providing richer contexts to Distributional Semantic Models improves by taking word interrelations into account but it would suffer from data sparsity. | They propose a Distributional Semantic Model that incorporates verb contexts as joint syntactic dependencies so that it emulates knowledge about event participants. | They show that representations obtained by the proposed model outperform more complex models on two verb similarity datasets with a limited training corpus. |
2021.acl-long.57 | In this paper , we propose Inverse Adversarial Training ( IAT ) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better . In contrast to standard adversarial training algorithms , IAT encourages the model to be sensitive to the perturbation in the dialogue history and... | Neural end-to-end dialogue models generate fluent yet dull and generic responses without taking dialogue histories into account due to the over-simplified maximum likelihood estimation objective. | They propose an algorithm which encourages to be sensitive to perturbations in dialogue histories and generates more diverse and consistent responses by applying penalization. | The proposed approach can model dialogue history better and generate more diverse and consistent responses on OpenSubtitles and DailyDialog. |
D09-1065 | demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI . This could be a very powerful technique for evaluating conceptual models extracted from corpora ; however , fMRI is expensive and imposes strong constraints on data collection . Following on expe... | The expensive cost of using fMRI hinders studies on the relationship between corpus-extracted models of semantic knowledge and neural activation patterns. | They propose to use EEG activation patterns instead of fMRI to reduce the cost. | They show that using EEG signals with corpus-based models, they can predict word level distinctions significantly above chance. |
D09-1085 | This paper introduces a new parser evaluation corpus containing around 700 sentences annotated with unbounded dependencies , from seven different grammatical constructions . We run a series of off-theshelf parsers on the corpus to evaluate how well state-of-the-art parsing technology is able to recover such dependencie... | While recent statistical parsers perform well on Penn Treebank, the results can be misleading due to several reasons originating from evaluation and datasets. | They propose a new corpus with unbounded dependencies from difference grammatical constructions. | Their evaluation of existing parsers with the proposed corpus shows lower scores than reported in previous works indicating a poor ability to recover unbounded dependencies. |
P12-1013 | Learning entailment rules is fundamental in many semantic-inference applications and has been an active field of research in recent years . In this paper we address the problem of learning transitive graphs that describe entailment rules between predicates ( termed entailment graphs ) . We first identify that entailmen... | Current inefficient algorithms aim to obtain entailment rules for semantic inference hindering the use of large resources. | They propose an efficient polynomial approximation algorithm that exploits their observation, entailment graphs have a "tree-like" property. | Their iterative algorithm runs by orders of magnitude faster than current exact state-of-the-art solutions while keeping close quality. |
D15-1054 | Sponsored search is at the center of a multibillion dollar market established by search technology . Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities . Lexical features derived from the text of both the query ... | Conventional word embeddings with a simple integration of click feedback information and averaging to obtain sentence representations do not work well for ad click prediction. | They propose several joint word embedding methods to leverage positive and negative click feedback which put query vectors close to relevant ad vectors. | The use of features obtained from the new models improves on a large sponsored search data of commercial Yahoo! search engine. |
D09-1072 | We propose a new model for unsupervised POS tagging based on linguistic distinctions between open and closed-class items . Exploiting notions from current linguistic theory , the system uses far less information than previous systems , far simpler computational methods , and far sparser descriptions in learning context... | Current approaches tackle unsupervised POS tagging as a sequential labelling problem and require a complete knowledge of the lexicon. | They propose to first identify functional syntactic contexts and then use them to make predictions for POS tagging. | The proposed method achieves equivalent performance by using 0.6% of the lexical knowledge used in baseline models. |
2021.naacl-main.458 | Non-autoregressive Transformer is a promising text generation model . However , current non-autoregressive models still fall behind their autoregressive counterparts in translation quality . We attribute this accuracy gap to the lack of dependency modeling among decoder inputs . In this paper , we propose CNAT , which ... | Non-autoregressive translation models fall behind their autoregressive counterparts in translation quality due to the lack of dependency modelling for the target outputs. | They propose a non-autoregressive transformer-based model which implicitly learns categorical codes as latent variables into the decoding to complement missing dependencies. | The proposed model achieves state-of-the-art without knowledge distillation and a competitive decoding speedup with an interactive-based model when coupled with knowledge distillation and reranking techniques. |
2021.emnlp-main.765 | The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction ( OpenRE ) . However , high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters can not explicitly align with the relat... | Even though high-dimensional vectors that can encode complex information used for relation extraction are not guaranteed to be consistent with relational semantic similarity. | They propose to use available relation labeled data to obtain relation-oriented representation by minimizing the distance between the same relation instances. | The proposed approach can reduce error rates significantly from the best models for open relation extraction. |
P10-1077 | Prior use of machine learning in genre classification used a list of labels as classification categories . However , genre classes are often organised into hierarchies , e.g. , covering the subgenres of fiction . In this paper we present a method of using the hierarchy of labels to improve the classification accuracy .... | Existing genre classification methods achieve high accuracy without regarding hierarchical structures exhibiting unrealistic experimental setups such as a limited number of genres and sources. | They propose a structural reformulation of the Support Vector Machine to take hierarchical information of genres into account by using similarities between different genres. | The proposed model outperforms non-hierarchical models on only one corpus and they discuss that it may be due to insufficient depth or inbalance of hierarchies. |
2020.acl-main.282 | The International Classification of Diseases ( ICD ) provides a standardized way for classifying diseases , which endows each disease with a unique code . ICD coding aims to assign proper ICD codes to a medical record . Since manual coding is very laborious and prone to errors , many methods have been proposed for the ... | Existing models that classify texts in medical records into the International Classification of Diseases reduce manual efforts however they ignore Code Hierarchy and Code Co-occurrence. | They propose a hyperbolic representation method to leverage the code hierarchy and a graph convolutional network to utilize the code-occurrence for automatic coding. | The proposed model outperforms state-of-the-art methods on two widely used datasets. |
N12-1028 | The important mass of textual documents is in perpetual growth and requires strong applications to automatically process information . Automatic titling is an essential task for several applications : ' No Subject ' e-mails titling , text generation , summarization , and so forth . This study presents an original appro... | Automatically titling documents is a complex task because of its subjectivity and titles must be informative, catchy and syntactically correct. | They propose to approach automatic titling by normalizing a verb phrase selected to be relevant into a noun phrase with morphological and semantic processing. | They show that the proposed normalizing process can produce informative and/or catchy titles but evaluations remain challenging due to its subjectivity. |
E14-1026 | We present a simple preordering approach for machine translation based on a featurerich logistic regression model to predict whether two children of the same node in the source-side parse tree should be swapped or not . Given the pair-wise children regression scores we conduct an efficient depth-first branch-and-bound ... | Preordering methods for machine translation systems that involve little or no human assistance, run on limited computational resources and use linguistic analysis tools are required. | They propose a logistic regression-based model with lexical features which predicts whether two children of the same node in the parse tree should be swapped. | Experiments on translation tasks from English to Japanese and Korean show the proposed method outperforms baseline preordering methods and runs 80 times faster. |
2020.acl-main.443 | There is an increasing interest in studying natural language and computer code together , as large corpora of programming texts become readily available on the Internet . For example , StackOverflow currently has over 15 million programming related questions written by 8.5 million users . Meanwhile , there is still a l... | Resources and fundamental techniques are missing for identifying software-related named entities such as variable names or application names within natural language texts. | They introduce a manually annotated named entity corpus for the computer programming domain and an attention-based model which incorporates a context-independent code token classifier. | The proposed model outperforms BiLSTM-CRF and fine-tuned BERT models by achieving a 79.10 F1 score for code and named entity recognition on their dataset which |
D14-1205 | Populating Knowledge Base ( KB ) with new knowledge facts from reliable text resources usually consists of linking name mentions to KB entities and identifying relationship between entity pairs . However , the task often suffers from errors propagating from upstream entity linkers to downstream relation extractors . In... | Existing pipeline approaches to populate Knowledge Base with new knowledge facts from texts suffer from error propagating from upstream entity linkers to downstream relation extractors. | They propose to formulate the problem in an Integer Linear Program to find an optimal configuration from the top k results of both tasks. | They show that the proposed framework can reduce error propagations and outperform competitive pipeline baselines with state-of-the-art relation extraction models. |
N19-1233 | Generative Adversarial Networks ( GANs ) are a promising approach for text generation that , unlike traditional language models ( LM ) , does not suffer from the problem of " exposure bias " . However , A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric .... | Generative Adversarial Networks-based text generation models do not suffer from the exposure bias problem, however; they cannot be evaluated as other language models with log-probability. | They propose a way to approximate distributions from GAN-based models' outputs so that they can be evaluated as standard language models. | When GAN-based models are compared using the same evaluation metric as proposed, they perform much worse than current best language models. |
P14-1064 | Statistical phrase-based translation learns translation rules from bilingual corpora , and has traditionally only used monolingual evidence to construct features that rescore existing translation candidates . In this work , we present a semi-supervised graph-based approach for generating new translation rules that leve... | The performance of statistical phrase-based translation is limited by the size of the available phrasal inventory both for resource rich and poor language pairs. | They propose a semi-supervised approach that produces new translation rules from monolingual data by phrase graph construction and graph propagation techniques. | Their method significantly improves over existing phrase-based methods on Arabic-English and Urdu-English systems when large language models are used. |
N18-1114 | We present a new approach to the design of deep networks for natural language processing ( NLP ) , based on the general technique of Tensor Product Representations ( TPRs ) for encoding and processing symbol structures in distributed neural networks . A network architecture -the Tensor Product Generation Network ( TPGN... | While Tensor Product Representation is a powerful model for obtaining vector embeddings for symbol structures, its application with deep learning models is still less investigated. | They propose a newly designed model that is based on Tensor Product Representations for encoding and processing words and sentences. | The Tensor Product Representation-based generative model outperforms LSTM models by evaluating on COCO image-caption dataset and also achieves high interpretability. |
N15-1159 | This paper describes a simple and principled approach to automatically construct sentiment lexicons using distant supervision . We induce the sentiment association scores for the lexicon items from a model trained on a weakly supervised corpora . Our empirical findings show that features extracted from such a machine-l... | While sentiment lexicons are useful for building accurate sentiment classification systems, existing methods suffer from low recall or interpretability. | They propose to use Twitter's noisy opinion labels as distant supervision to learn a supervised polarity classifier and use it to obtain sentiment lexicons. | Using the obtained lexicon with an existing model achieves the state-of-the-art on the SemEval-13 message level task and outperforms baseline models in several other datasets. |
D07-1036 | Parallel corpus is an indispensable resource for translation model training in statistical machine translation ( SMT ) . Instead of collecting more and more parallel training corpora , this paper aims to improve SMT performance by exploiting full potential of the existing parallel corpora . Two kinds of methods are pro... | Statistical machine translation systems require corpora limited in domain and size, and a model trained on one domain does not perform well on other domains. | They propose offline and online methods to maximize the potential of available corpora by weighting training samples or submodules using an information retrieval model. | The proposed approaches improve translation quality without additional resources using even less data, further experiments with larger training data show that the methods can scale. |
P03-1015 | The paper describes two parsing schemes : a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar . It discusses several ways to combine them and presents evaluation results for the two individual approaches and their combination . An underspecification scheme for the... | Combining different methods often achieves the best results especially combinations of shallow and deep can realize both interpretability and good results. | They propose several ways to combine a machine learning-based shallow method and a hand-crafted grammar-based cascaded method for parsers. | Evaluations on a treebank of German newspaper texts show that the proposed method achieves substantial gain when there are ambiguities. |
N09-1062 | Tree substitution grammars ( TSGs ) are a compelling alternative to context-free grammars for modelling syntax . However , many popular techniques for estimating weighted TSGs ( under the moniker of Data Oriented Parsing ) suffer from the problems of inconsistency and over-fitting . We present a theoretically principle... | Although Probabilistic Context Free Grammers-based models are currently successful, they suffer from inconsistency and over-fitting when learning from a treebank. | They propose a Probabilistic Tree Substitution Grammer model with a Bayesian-based algorithm for training to accurately model the data and to keep the grammar simple. | The proposed model learns local structures for latent linguistic phenomena outperforms standard methods and is comparable to state-of-the-art methods on small data. |
2020.emnlp-main.505 | News headline generation aims to produce a short sentence to attract readers to read the news . One news article often contains multiple keyphrases that are of interest to different users , which can naturally have multiple reasonable headlines . However , most existing methods focus on the single headline generation .... | Existing news headline generation models only focus on generating one output even though news articles often have multiple points. | They propose a multi-source transformer decoder and train it using a new large-scale keyphrase-aware news headline corpus built from a search engine. | Their model outperforms strong baselines on their new real-world keyphrase-aware headline generation dataset. |
N16-1103 | Universal schema builds a knowledge base ( KB ) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns observed in raw text . In most previous applications of universal schema , each textual pattern is represented as a single embedding , preventing generalization to... | Existing approaches to incorporate universal schemas for automatic knowledge base construction has limitation in generalization to unseen inputs from training time. | They propose to combine universal schemas and neural network-based deep encoders to achieve generalization to an unseen language without additional annotations. | The proposed approach outperforms existing methods on benchmarks in English and Spanish while having no hand-coded rules or training data for Spanish. |
E17-1022 | We propose UDP , the first training-free parser for Universal Dependencies ( UD ) . Our algorithm is based on PageRank and a small set of head attachment rules . It features two-step decoding to guarantee that function words are attached as leaf nodes . The parser requires no training , and it is competitive with a del... | For dependency parsing, unsupervised methods struggle with learning relations that match conventions of the test data and supervised counterparts suffer from word order target adaptation. | They propose an unsupervised approach based on PageRank and a set of head attachment rules that solely depend on explicit part-of-speech constraints from Universal Dependencies. | The proposed linguistically sound method performs competitively with a delexicalized transfer system while having few parameters and robustness to domain changes across languages. |
P19-1081 | We study a conversational reasoning model that strategically traverses through a largescale common fact knowledge graph ( KG ) to introduce engaging and contextually diverse entities and attributes . For this study , we collect a new Open-ended Dialog ↔ KG parallel corpus called OpenDialKG , where each utterance from 1... | Using a large knowledge base for dialogue systems is intractable or not scalable which calls for methods that prune search space for entities. | They provide an open-ended dialogue corpus where each utterance is annotated with entities and paths and propose a model that works on this data structure. | The proposed model can produce more natural responses than state-of-the-art models on automatic and human evaluation, and generated knowledge graph paths provide explainability. |
D12-1011 | Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities . Yet for many types of entities , such as restaurants and cult movies , relational databases exist that contain far more extensive information than Wikipedia . This paper introduces a new task , call... | Existing approaches to disambiguate named entities solely use Wikipedia as a catalogue however Many kinds of named entities are missed in Wikipedia. | They propose a task where systems need to reference arbitrary databases for finding named entities not only Wikipedia, together with methods to achieve domain adaptation. | A mixture of two domain adaptation methods outperforms existing systems that only rely on Wikipedia for their new Open-DB Named Entity Disambiguation task. |
2020.emnlp-main.308 | Solving algebraic word problems has recently emerged as an important natural language processing task . To solve algebraic word problems , recent studies suggested neural models that generate solution equations by using ' Op ( operator / operand ) ' tokens as a unit of input / output . However , such a neural model suf... | Neural models largely underperform hand-crafted feature-based models on algebraic word datasets such as ALG514 because of two issues namely expression fragmentation and operand-context separation. | They propose a model which generates an operator and required operands and applies operand-context pointers to resolve the expression fragmentation and operand-context separation issues respectively. | The proposed model performs comparable results to the state-of-the-art models with hand-crafted features and outperforms neural models by 40% on three datasets. |
P98-1104 | In this paper I will report the result of a quantitative analysis of the dynamics of the constituent elements of Japanese terminology . In Japanese technical terms , the linguistic contribution of morphemes greatly differ according to their types of origin . To analyse this aspect , a quantitative method is applied , w... | Static quantitative descriptions are not sufficient to analyse Japanese terminology because of the dynamic nature of samples calling for a method beyond the sample size. | They apply a quantitative method which can characterise the dynamic nature of morphemes using a small sample of Japanese terminology. | They show that the method can successfully analyze the dynamic nature of the morphemes in Japanese terminology with suitable means. |
2021.acl-long.420 | We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa . Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage , so that they suffer from discrepancy between the two stages . Such a problem would lead to th... | Existing ways of injecting syntactic knowledge into pretraining models cause discrepancies between pretraining and fine-tuning and require expensive annotation. | They propose to inject syntactic features obtained by an off-the-shelf parser into pretraining models coupled with a new syntax-aware attention layer. | The proposed model achieves state-of-the-art in relation classification, entity typing, and question answering tasks. |
P16-1067 | This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components . The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships . For this purpose , we adopt Hidden Markov Model ( HMM ) and construct a sequential... | There is no method for multiauthor segmentation of a document into author components which can be applied to authorship verification, plagiarism detection and author attribution. | They propose a HMM-based sequential probabilistic model that captures the dependencies of sequential sentences and their authors coupled with an unsupervised initialization method. | Experiments with artificial and authentic scientific document datasets show that the proposed model outperforms existing methods and also be able to provide confidence scores. |
N13-1083 | We investigate two systems for automatic disfluency detection on English and Mandarin conversational speech data . The first system combines various lexical and prosodic features in a Conditional Random Field model for detecting edit disfluencies . The second system combines acoustic and language model scores for detec... | Existing works on detecting speech disfluency which can be a problem for downstream processing and creating transcripts only focus on English. | They evaluate a Conditional Random Field-based edit disfluency detection model and a system which combines acoustic and language model that detects filled pauses in Mandarin. | Their system comparisons in English and Mandarin show that combining lexical and prosodic features achieves improvements in both languages. |
P01-1026 | We propose a method to generate large-scale encyclopedic knowledge , which is valuable for much NLP research , based on the Web . We first search the Web for pages containing a term in question . Then we use linguistic patterns and HTML structures to extract text fragments describing the term . Finally , we organize ex... | Existing methods that extract encyclopedic knowledge from the Web output unorganized clusters of term descriptions not necessarily related to explicit criteria while clustering is performed. | They propose to use word senses and domains for organizing extracted term descriptions on questions extracted from the Web to improve the quality. | The generated encyclopedia is applied to a Japanese question and answering system and improves over a system which solely depends on a dictionary. |
D15-1028 | Research on modeling time series text corpora has typically focused on predicting what text will come next , but less well studied is predicting when the next text event will occur . In this paper we address the latter case , framed as modeling continuous inter-arrival times under a log-Gaussian Cox process , a form of... | Modeling the event inter-arrival time of tweets is challenging due to complex temporal patterns but few works aim to predict the next text event occurrence. | They propose to apply a log-Gaussian Cox process model which captures the varying arriving rate over time coupled with the textual contents of tweets. | The proposed model outperforms baseline models on an inter-arrival time prediction task around a riots rumour and shows that it improves with textual features. |
P18-1222 | Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , conventional text embedding methods suffer from information loss if directly adapted to hyper-documents . In this paper , we propose a general... | Existing text embedding methods do not take structures of hyper-documents into account losing useful properties for downstream tasks. | They propose an embedding method for hyper-documents that learns citation information along with four criteria to assess the properties the models should preserve. | The proposed model satisfies all of the introduced criteria and performs two tasks in the academic domain better than existing models. |
N18-1108 | Recurrent neural networks ( RNNs ) have achieved impressive results in a variety of linguistic processing tasks , suggesting that they can induce non-trivial properties of language . We investigate here to what extent RNNs learn to track abstract hierarchical syntactic structure . We test whether RNNs trained with a ge... | Previous work only shows that RNNs can handle constructions that require hierarchical structure when explicit supervision on the target task is given. | They introduce a probing method for syntactic abilities to evaluate long-distance agreement on standard and nonsensical sentences in multiple languages with different morphological systems. | The RNNs trained on an LM objective can solve long-distance agreement problems well even on nonsensical sentences consistently across languages indicating their deeper grammatical competence. |
D09-1115 | Current system combination methods usually use confusion networks to find consensus translations among different systems . Requiring one-to-one mappings between the words in candidate translations , confusion networks have difficulty in handling more general situations in which several words are connected to another se... | System combination methods based on confusion networks only allow word level 1-to-1 mappings, and some workarounds cause another type of problem such as degeneration. | They propose to use lattices to combine systems that enable to process of a sequence of words rather than one word that can mitigate degeneration. | They show that their approach significantly outperforms the state-of-the-art confusion-network-based systems on Chinese-to-English translation tasks. |
E17-1110 | The growing demand for structured knowledge has led to great interest in relation extraction , especially in cases with limited supervision . However , existing distance supervision approaches only extract relations expressed in single sentences . In general , cross-sentence relation extraction is under-explored , even... | Existing distance supervision methods for relation extraction cannot capture relations crossing the sentence boundary which is important in specialized domains with long-tail knowledge. | They propose a method for applying distance supervision to cross-sentence relation extraction by adopting a document-level graph representation that incorporates intra-sentential dependencies and inter-sentential relations. | Experiments on extracting drug-gene interactions from biomedical literature show that the proposed method doubles the performance of single-sentence extraction methods. |
P07-1026 | Convolution tree kernel has shown promising results in semantic role classification . However , it only carries out hard matching , which may lead to over-fitting and less accurate similarity measure . To remove the constraint , this paper proposes a grammardriven convolution tree kernel for semantic role classificatio... | Despite its success in semantic role classification, convolution tree kernels based on the hard matching between two sub-trees suffer from over-fitting. | They propose to integrate a linguistically motivated grammar-baed convolution tree kernel into a standard tree kernel to achieve better substructure matching and tree node matching. | The new grammar-driven tree kernel significantly outperforms baseline kernels on the CoNLL-2005 task. |
E09-1032 | We explore the problem of resolving the second person English pronoun you in multi-party dialogue , using a combination of linguistic and visual features . First , we distinguish generic and referential uses , then we classify the referential uses as either plural or singular , and finally , for the latter cases , we i... | Although the word "you" is frequently used and has several possible meanings, such as reference or generic, it is not well studied yet. | They first manually automatically separate the word "you" between generic and referential, then later use a multimodal system for automation. | They show that visual features can help distinguish the word "you" in multi-party conversations. |
P10-1139 | There is a growing research interest in opinion retrieval as on-line users ' opinions are becoming more and more popular in business , social networks , etc . Practically speaking , the goal of opinion retrieval is to retrieve documents , which entail opinions or comments , relevant to a target subject specified by the... | Existing approaches to the opinion retrieval task represent documents using bag-of-words disregarding contextual information between an opinion and its corresponding text. | They propose a sentence-based approach which captures both inter and intra sentence contextual information combined with a unified undirected graph. | The proposed method outperforms existing approaches on the COAE08 dataset showing that word pairs can represent information for opinion retrieval well. |
N03-1024 | We describe a syntax-based algorithm that automatically builds Finite State Automata ( word lattices ) from semantically equivalent translation sets . These FSAs are good representations of paraphrases . They can be used to extract lexical and syntactic paraphrase pairs and to generate new , unseen sentences that expre... | Existing approaches to represent paraphrases as sets or pairs of semantically equivalent words, phrases and patterns that are weak for text generation purposes. | They propose a syntax-based algorithm that builds Finite State Automata from translation sets which are good representations of paraphrases. | Manual and automatic evaluations show that the representations extracted by the proposed method can be used for automatic translation evaluations. |
P18-1159 | While sophisticated neural-based techniques have been developed in reading comprehension , most approaches model the answer in an independent manner , ignoring its relations with other answer candidates . This problem can be even worse in open-domain scenarios , where candidates from multiple passages should be combine... | Existing models for reading comprehension do not consider multiple answer candidates which can be problematic when they need to fuse information from multiple passages. | They propose to approach reading comprehension with an extract-then-select procedure, where a model learns two tasks jointly using latent variables and reinforcement learning. | The proposed model can fuse answer candidates from multiple candidates and significantly outperform existing models on two open-domain reading comprehension tasks. |
W06-1672 | We present two discriminative methods for name transliteration . The methods correspond to local and global modeling approaches in modeling structured output spaces . Both methods do not require alignment of names in different languages -their features are computed directly from the names themselves . We perform an exp... | The name transliteration task aims to transcribe extracted names into English, and since current extraction systems are fairly fast, applicable techniques for transliteration are limited. | They present two discriminative methods that learn a map function from one language into another using a dictionary without the notion of alignment. | The proposed methods outperform state-of-the-art probabilistic models on name transliteration from Arabic, Korean, and Russian to English, and the global discriminative modelling performs the best. |
2022.acl-long.393 | Motivated by the success of T5 ( Text-To-Text Transfer Transformer ) in pre-trained natural language processing models , we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech / text representation learning . The SpeechT5 framework consists of a shared en... | Existing speech pre-training methods ignore the importance of textual data and solely depend on encoders leaving the decoder out of pre-training for generation tasks. | They propose a unified-modal encoder-decoder framework with shared and modal-specific networks for self-supervised speech and text representation learning by using unlabeled text and speech corpus. | The fine-tuned proposed model is evaluated on a variety of spoken processing tasks and outperforms state-of-the-art models on voice conversion speaker identification tasks. |
D13-1158 | Recent studies on extractive text summarization formulate it as a combinatorial optimization problem such as a Knapsack Problem , a Maximum Coverage Problem or a Budgeted Median Problem . These methods successfully improved summarization quality , but they did not consider the rhetorical relations between the textual u... | Existing optimization-based methods for extractive summarization do not consider the rhetorical relations between textual units leading to generating uncoherent summaries or missing significant textual units. | They propose to first transform a rhetorical discourse tree into a dependency-based tree and then trim it as a Tree Knapsack Problem. | The proposed method achieves the highest ROUGE-1,2 scores on 30 documents selected from the RST Discourse Treebank Corpus. |
D19-1098 | Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have achieved state-of-the-art results on diverse NLP tasks . However , it is unclear what the learned attention captures . The attention computed by attention heads seems not to match human intuitions about hierarchical structures ... | This is unclear what attention heads in pre-training transformers models capture and it seems not to match human intuitions about hierarchical structures. | They propose to add an extra constraint to attention heads of the bidirectional Transformer encoder and a module induces tree structures from raw texts. | The proposed model achieves better unsupervised tree structure induction, language modelling, and more explainable attention scores which are coherent to human expert annotations. |
D09-1131 | This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences . Chinese words are classified into eight morphological types by two proposed classifiers , CRF classifier and SVM classifier . Experiments show that the injection of morphological information... | There is not much work on applying morphological information in opinion extraction in Chinese. | They propose to utilize morphological and syntactic features for Chinese opinion analysis on word and sentence levels. | They show that using morphological structures helps opinion analysis in Chinese, outperforming the existing bat-of-character approach and the dictionary-based approach. |
P19-1252 | In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / following community , and the user 's social... | Existing systems only use limited information from the tweets network to perform occupation classification. | They extend existing Twitter occupation classification graph-based models to exploit content information by adding textual data to existing datasets. | They show that textual feature enables graph neural networks to predict Twitter user occupation well even with a limited amount of training data. |
P06-1073 | Short vowels and other diacritics are not part of written Arabic scripts . Exceptions are made for important political and religious texts and in scripts for beginning students of Arabic . Script without diacritics have considerable ambiguity because many words with different diacritic patterns appear identical in a di... | Short vowels and other diacritics are not expressed in written Arabic, making it difficult to read for beginner readers or system developments. | They propose an approach that uses maximum entropy to restore diacritics in Arabic documents by learning relations between a wide varieties of features. | They show that by taking various kinds of features their system outperforms the existing state-of-the-art decritization model. |
2021.eacl-main.251 | Current state-of-the-art systems for joint entity relation extraction ( Luan et al . , 2019 ; Wadden et al . , 2019 ) usually adopt the multi-task learning framework . However , annotations for these additional tasks such as coreference resolution and event extraction are always equally hard ( or even harder ) to obtai... | Current joint entity relation extraction models follow a multitask learning setup however datasets with multiple types of annotation are not available for many domains. | They propose to pre-train a language model entity relation extraction with four newly introduced objective functions which utilize automatically obtained annotations by NER models. | The models pre-trained by the proposed method significantly outperform BERT and current state-of-the-art models on three entity relation extraction datasets. |
P16-1089 | We present the Siamese Continuous Bag of Words ( Siamese CBOW ) model , a neural network for efficient estimation of highquality sentence embeddings . Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings . However , word embeddings... | While an average of word embeddings has proven to be successful as sentence-level representations, it is suboptimal because they are not optimized to represent sentences. | They propose to train word embeddings directly for the purpose of being averaged by predicting sounding sentences from a sentence representation using unlabeled data. | Evaluations show that their word embeddings outperform existing methods in 14 out of 20 datasets and they are stable in choice of parameters. |
D17-1222 | We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoderdecoder model equipped with a deep recurrent generative decoder ( DRGN ) . Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summ... | Although humans follow inherent structures in summary writing, currently there are no abstractive summarization models which take latent structure information and recurrent dependencies into account. | They propose a Variational Auto-Encoder-based sequence-to-sequence oriented encoder-decoder model with a deep recurrent generative decoder which learns latent structure information implied in the target summaries. | The proposed model outperforms the state-of-the-art models on some datasets in different languages. |
2021.naacl-main.72 | Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models . Attention redundancy has been observed among attention heads but has not been deeply studied in the literature . Using BERT-base model as an example , this paper provides a comprehensive study on attention redundancy wh... | While there are works that report a redundancy among attention heads in modern language models, no works investigate its pattern deeply. | They perform token and sentence level analysis on redundancy matrices from pre-trained and fine-tuned BERT-base models and further propose a pruning method based on findings. | They find that many heads are redundant regardless of phase and task, and show the proposed pruning method can perform robustly. |
D17-1220 | Comprehending lyrics , as found in songs and poems , can pose a challenge to human and machine readers alike . This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts , and provide commentary to aid readers in reaching the correct interpretation . We introduce the t... | Because of its creative nature, understanding lyrics can be challenging both for humans and machines. | They propose a task of automated lyric annotation with a dataset collected from an online platform which explains lyrics to readers. | They evaluate translation and retrieval models with automatic and human evaluation and show that different models capture different aspects well. |
H05-1023 | Most statistical translation systems are based on phrase translation pairs , or " blocks " , which are obtained mainly from word alignment . We use blocks to infer better word alignment and improved word alignment which , in turn , leads to better inference of blocks . We propose two new probabilistic models based on t... | Automatic word alignment used in statistical machine translations, does not achieve satisfactory performance in some language pairs such as because of the limitations of HMMs. | They propose to use phrase translation pairs to get better word alignments using two new probabilistic models based on EM-algorithm that localizes the alignments. | The proposed models outperform IBM Model-4 by 10%, both on small and large training setups, and the translation models based on this result improve qualities. |
E06-1051 | We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information . We use a combination of kernel functions to integrate two different information sources : ( i ) the whole sentence where the relation appears , and ( ii ) the local contexts aroun... | Deep linguistic features obtained by parsers are not always robust and available for limited languages and domains, however; applications of shallow features are under investigated. | They propose an approach for entity relation extraction using shallow linguistic information such as tokenization, sentence splitting, Part-of-Speech tagging and lemmatization coupled with kernel functions. | Evaluations of two biomedical datasets show that the proposed method outperforms existing systems which depend on syntactic or manually annotated semantic information. |
N09-1032 | Domain adaptation is an important problem in named entity recognition ( NER ) . NER classifiers usually lose accuracy in the domain transfer due to the different data distribution between the source and the target domains . The major reason for performance degrading is that each entity type often has lots of domainspec... | Named entity recognition classifiers lose accuracy in domain transfers because each entity type has domain-specific term representations and existing approaches require expensive labeled data. | They propose to capture latent semantic associations among words in the unlabeled corpus and use them to tune original named entity models. | The proposed model improves the performance on the English and Chinese corpus across domains especially on each NE type recognition. |
D09-1030 | Manual evaluation of translation quality is generally thought to be excessively time consuming and expensive . We explore a fast and inexpensive way of doing it using Amazon 's Mechanical Turk to pay small sums to a large number of non-expert annotators . For $ 10 we redundantly recreate judgments from a WMT08 translat... | Because of the high cost required for manual evaluation, most works rely on automatic evaluation metrics although there are several drawbacks. | They investigate whether judgements by non-experts from Amazon's Mechanical Turk can be a fast and inexpensive means of evaluation for machine translation systems. | They found that non-expert judgements with high agreement correlate better with gold standard judgements than BLEU while keeping the cost low. |
D18-1133 | State-of-the-art networks that model relations between two pieces of text often use complex architectures and attention . In this paper , instead of focusing on architecture engineering , we take advantage of small amounts of labelled data that model semantic phenomena in text to encode matching features directly in th... | State-of-the-art models that model relations between two texts use complex architectures and attention which requires a long time and large data at training. | They propose a method that directly models higher-level semantic links between two texts that are annotated by a fast model. | The proposed model outperforms a tree kernel model and complex neural models while keeping the model simple and the training fast. |
2021.emnlp-main.411 | Language representations are known to carry certain associations ( e.g. , gendered connotations ) which may lead to invalid and harmful predictions in downstream tasks . While existing methods are effective at mitigating such unwanted associations by linear projection , we argue that they are too aggressive : not only ... | Existing methods that remove harmful stereotypical associations from word embeddings either require inefficient retraining or remove information which should be retained. | They propose a method which orthogonalizes and rectifies incorrectly associated subspaces of concepts in an embedding space and a metric for evaluating information retention. | NLI-based evaluation on gender-occupation associations shows that the proposed approach is well-balanced ensuring semantic information is retained in the embeddings while mitigating biases. |
2020.acl-main.75 | Humor plays an important role in human languages and it is essential to model humor when building intelligence systems . Among different forms of humor , puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity . However , identifying and modeling puns are challeng... | Puns involve implicit semantic or phonological tricks however there is no general framework to model these two types of signals as a whole. | They propose to jointly model contextualized word embeddings and phonological word representations by breaking each word into a sequence of phonemes for pun detection. | The proposed approach outperforms the state-of-the-art methods in pun detection and location tasks. |
D10-1083 | Part-of-speech ( POS ) tag distributions are known to exhibit sparsity -a word is likely to take a single predominant tag in a corpus . Recent research has demonstrated that incorporating this sparsity constraint improves tagging accuracy . However , in existing systems , this expansion come with a steep increase in mo... | Assuming there is only one tag for a word is a powerful heuristic for Part-of-speech tagging but incorporating this into a model leads to complexity. | They propose an unsupervised method that directly incorporates a one-tag-per-word assumption into a HMM-based model. | Their proposed method reduces the number of model parameters which results in faster training speed and also outperforms more complex systems. |
P10-1072 | We present a game-theoretic model of bargaining over a metaphor in the context of political communication , find its equilibrium , and use it to rationalize observed linguistic behavior . We argue that game theory is well suited for modeling discourse as a dynamic resulting from a number of conflicting pressures , and ... | Metaphors used in political arguments provide elaborate conceptual correspondences with a tendency of politicians to be compelled by the rival's metaphorical framework to be explained. | They propose a game-theoric model of bargaining over a metaphor which is suitable to model its dynamics and use to rationalize observed linguistic behavior. | They show that the proposed framework can rationalize political communications with the use of extended metaphors based on the characteristics of the setting. |
2020.acl-main.47 | We examine a methodology using neural language models ( LMs ) for analyzing the word order of language . This LM-based method has the potential to overcome the difficulties existing methods face , such as the propagation of preprocessor errors in count-based methods . In this study , we explore whether the LMbased meth... | Linguistical approaches to analyze word order phenomena have scalability and preprocessor error propagation problems, and the use of language models is limited in English. | They validate language models as a tool to study word order in Japanese by examining the relationship between canonical word order and generation probability. | They show that language models have sufficient word order knowledge in Japanese to be used as a tool for linguists. |
2021.naacl-main.150 | A conventional approach to improving the performance of end-to-end speech translation ( E2E-ST ) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition ( ASR ) and neural machine translation ( NMT ) tasks . However , since the input modalities are different ... | Existing works on end-to-end speech recognition models use the source transcriptions for performance improvements but it is challenging due to the modality gap. | They propose a bidirectional sequence knowledge distillation which learns from text-based NMT systems with a single decoder to enhance the model to capture semantic representations. | Evaluations on autoregressive and non-autoregressive models show that the proposed method improves in both directions and the results are consistent in different model sizes. |
N07-1072 | This paper explores the problem of computing text similarity between verb phrases describing skilled human behavior for the purpose of finding approximate matches . Four parsers are evaluated on a large corpus of skill statements extracted from an enterprise-wide expertise taxonomy . A similarity measure utilizing comm... | Existing systems for skilled expertise matching use exact matching between skill statements resulting in missing good matches and calling for a system with approximate matching. | They evaluate four different parsers to take structural information into consideration by matching skill statements on corresponding semantic roles from generated parse trees. | The proposed similarity measure outperforms a standard statistical information-theoretic measure and is comparable to a human agreement. |
2020.acl-main.573 | Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations . Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent tr... | Storing histories of examples is shown to be effective for continual relation learning however existing methods suffer from overfitting to memorize a few old memories. | They propose a human memory mechanism inspired by memory activation and reconsolidation method which aims to keep a stable understanding of old relations. | The proposed method mitigates catastrophic forgetting of old relations and achieves state-of-the-art on several relation extraction datasets showing it can use memorized examples. |
2021.emnlp-main.66 | This paper proposes to study a fine-grained semantic novelty detection task , which can be illustrated with the following example . It is normal that a person walks a dog in the park , but if someone says " A man is walking a chicken in the park , " it is novel . Given a set of natural language descriptions of normal s... | Existing works on novelty or abnormally detection are coarse-grained only focusing on the document or sentence level as a text classification task. | They propose a fine-grained semantic novelty detection problem where systems detect whether a textual description is a novel fact, coupled with a graph attention-based model. | The proposed model outperforms 11 baseline models on the created dataset from an image caption dataset for the proposed task by large margins. |
P12-1096 | Long distance word reordering is a major challenge in statistical machine translation research . Previous work has shown using source syntactic trees is an effective way to tackle this problem between two languages with substantial word order difference . In this work , we further extend this line of exploration and pr... | Long distance word reordering remains a challenge for statistical machine translation and existing approaches do it during the preprocessing. | They propose a ranking-based reordering approach where the ranking model is automatically derived from the word aligned parallel data using a syntax parser. | Large scale evaluation of Japanese-English and English-Japanese shows that the proposed approach significantly outperforms the baseline phrase-based statistical machine translation system. |
D08-1038 | How can the development of ideas in a scientific field be studied over time ? We apply unsupervised topic modeling to the ACL Anthology to analyze historical trends in the field of Computational Linguistics from 1978 to 2006 . We induce topic clusters using Latent Dirichlet Allocation , and examine the strength of each... | How topics or ideas have developed over time in NLP community remains unknown while there are analysis over the ACL anthology citation graph. | They propose to use Latent Dirichlet Allocation for studying topic shift over time and a model to compute the diversity of ideas and topic entropy. | They found that COLING has more diversity than ACL, but all the conferences are becoming to cover more topics over time, and applications increase generally. |
P17-1024 | In this paper , we aim to understand whether current language and vision ( LaVi ) models truly grasp the interaction between the two modalities . To this end , we propose an extension of the MS-COCO dataset , FOIL-COCO , which associates images with both correct and ' foil ' captions , that is , descriptions of the ima... | Despite the success of language and vision models on visual question answering tasks, what these models are learning remains unknown because of coarse-grained datasets. | They propose to automatically inject one mistake to captions in the MS-COCO dataset as a foil word and three diagnostic tasks to study models' behaviors. | Using the introduced dataset, they find that best performing models fail on the proposed tasks indicating their abilities to integrate two modalities. |
D17-1323 | Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web . Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found ... | Language is used for visual recognition problems such as captioning to improve performance however it can also encode social biases found in web corpora. | They propose a framework to quantify bias for visual semantic role labelling and multilabel object classification and a constraint inference framework to calibrate existing models. | They find that existing datasets contain gender bias the use of text can amplify it, and the proposed framework can reduce bias without performance loss. |
P16-1177 | We present a pairwise context-sensitive Autoencoder for computing text pair similarity . Our model encodes input text into context-sensitive representations and uses them to compute similarity between text pairs . Our model outperforms the state-of-the-art models in two semantic retrieval tasks and a contextual word si... | Existing approaches for textual representation learning only use local information without contexts which capture global information that can guide neural networks in generating accurate representations. | They propose a pairwise context-sensitive Autoencoder which integrates sentential or document context for computing text pair similarity. | The proposed model outperforms the state-of-the-art models in two retrieval and word similarity tasks and an unsupervised version performs comparable with several supervised baselines. |
D09-1066 | Distance-based ( windowless ) word assocation measures have only very recently appeared in the NLP literature and their performance compared to existing windowed or frequency-based measures is largely unknown . We conduct a largescale empirical comparison of a variety of distance-based and frequency-based measures for ... | The performance of new windowless word association measures which take the number of tokens separating words into account remains unknown. | They conduct large-scale empirical comparisons of window-based and windowless association measures for the reproduction of syntagmatic human association norms. | The best windowless measures perform on part with best window-based measures on correlation with human association scores. |
D09-1042 | This paper presents an effective method for generating natural language sentences from their underlying meaning representations . The method is built on top of a hybrid tree representation that jointly encodes both the meaning representation as well as the natural language in a tree structure . By using a tree conditio... | While hybrid trees are shown to be effective for semantic parsing, their application for text generation is under explored. | They propose a phrase-level tree conditional random field that uses a hybrid tree of a meaning representation for the text generation model. | Experiments in four languages with automatic evaluation metrics show that the proposed conditional random field-based model outperforms the previous state-of-the-art system. |
P98-1081 | In this paper we examine how the differences in modelling between different data driven systems performing the same NLP task can be exploited to yield a higher accuracy than the best individual system . We do this by means of an experiment involving the task of morpho-syntactic wordclass tagging . Four well-known tagge... | Different data driven approaches tend to produce different errors and their qualities are limited due to the learning method and available training material. | They propose to combine four different modelling methods for the task of morpho-syntactic wordclass tagging by using several voting strategies and second stage classifiers. | All combinations outperform the best component, with the best one showing a 19.1% lower error rate and raising the performance ceiling. |
2020.emnlp-main.500 | Adversarial attacks for discrete data ( such as texts ) have been proved significantly more challenging than continuous data ( such as images ) since it is difficult to generate adversarial samples with gradient-based methods . Current successful attack methods for texts usually adopt heuristic replacement strategies o... | Generating adversarial samples with gradient-based methods for text data is because of its discrete nature and existing complicated heuristic-based methods suffer from finding optimal solutions. | They propose to use BERT to generate adversarial samples by first finding the valuable words and generating substitutes for these words in a semantic-preserving way. | The proposed method outperforms state-of-the-art methods in success rate and perturb percentage while preserving fluency and sematic of generated samples with low cost. |
E17-1060 | We investigate the generation of onesentence Wikipedia biographies from facts derived from Wikidata slot-value pairs . We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries . Our model incorporates a novel secondary objective that helps ensure it ge... | Wikipedia and other collaborative knowledge bases have coverage and quality issues especially on a long tail of specialist topics. | They propose a recurrent neural network sequence-to-sequence model with an attention mechanism trained on a multi-task autoencoding objective to generate one-sentence Wikipedia biographies from Wikidata. | The proposed model achieves 41 BLEU score outperforming the baseline model and human annotators prefer the 40% of outputs as good as Wikipedia gold references. |
D08-1050 | Most state-of-the-art wide-coverage parsers are trained on newspaper text and suffer a loss of accuracy in other domains , making parser adaptation a pressing issue . In this paper we demonstrate that a CCG parser can be adapted to two new domains , biomedical text and questions for a QA system , by using manually-anno... | Most existing parsers are tuned for newspaper texts making them limited in applicable domains. | They propose a method to adapt a CCG parser to new domains using manually-annotated data only at POS and lexical category levels. | The proposed method achieves comparable results to in-domain parsers without expensive full annotations on biomedical texts and questions that are rare in existing benchmark datasets. |
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