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SciEvents

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ACL
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs. However, most of them have limitations to efficiently exploit relevant context due to the lack of a powerful mechanism for modeling interactions between the slot and t...
dd3f7e548d5355eec14fc82d8e4428f2
2,020
[ "recent studies in dialogue state tracking ( dst ) leverage historical information to determine states which are generally represented as slot - value pairs .", "however , most of them have limitations to efficiently exploit relevant context due to the lack of a powerful mechanism for modeling interactions betwee...
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ACL
Diversifying Dialog Generation via Adaptive Label Smoothing
Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in the literature. Although existing approaches such as label smoothing can alleviate this issue, they fail to adapt to diverse dialog context...
310f49693c086ccb920ab06af887e997
2,021
[ "neural dialogue generation models trained with the one - hot target distribution suffer from the over - confidence issue , which leads to poor generation diversity as widely reported in the literature .", "although existing approaches such as label smoothing can alleviate this issue , they fail to adapt to diver...
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ACL
Contextual Embeddings: When Are They Worth It?
We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline—random word embeddings—focusing on the impact of the training set size and the linguistic properties of the task. Surprisin...
c7d75e3daa674b8c9e055723ddc6c84d
2,020
[ "we study the settings for which deep contextual embeddings ( e . g . , bert ) give large improvements in performance relative to classic pretrained embeddings ( e . g . , glove ) , and an even simpler baseline — random word embeddings — focusing on the impact of the training set size and the linguistic properties ...
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ACL
Span-based Semantic Parsing for Compositional Generalization
Despite the success of sequence-to-sequence (seq2seq) models in semantic parsing, recent work has shown that they fail in compositional generalization, i.e., the ability to generalize to new structures built of components observed during training. In this work, we posit that a span-based parser should lead to better co...
e74cd1cfd90f9fbc8841509b299ac931
2,021
[ "despite the success of sequence - to - sequence ( seq2seq ) models in semantic parsing , recent work has shown that they fail in compositional generalization , i . e . , the ability to generalize to new structures built of components observed during training .", "in this work , we posit that a span - based parse...
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ACL
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Dialogue state tracker is responsible for inferring user intentions through dialogue history. Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce red...
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[ "dialogue state tracker is responsible for inferring user intentions through dialogue history .", "previous methods have difficulties in handling dialogues with long interaction context , due to the excessive information .", "we propose a dialogue state tracker with slot attention and slot information sharing (...
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ACL
Revisiting the Compositional Generalization Abilities of Neural Sequence Models
Compositional generalization is a fundamental trait in humans, allowing us to effortlessly combine known phrases to form novel sentences. Recent works have claimed that standard seq-to-seq models severely lack the ability to compositionally generalize. In this paper, we focus on one-shot primitive generalization as int...
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2,022
[ "compositional generalization is a fundamental trait in humans , allowing us to effortlessly combine known phrases to form novel sentences .", "recent works have claimed that standard seq - to - seq models severely lack the ability to compositionally generalize .", "in this paper , we focus on one - shot primit...
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ACL
Importance-based Neuron Allocation for Multilingual Neural Machine Translation
Multilingual neural machine translation with a single model has drawn much attention due to its capability to deal with multiple languages. However, the current multilingual translation paradigm often makes the model tend to preserve the general knowledge, but ignore the language-specific knowledge. Some previous works...
dc50d12ff1a5cc14a989603a1eb17539
2,021
[ "multilingual neural machine translation with a single model has drawn much attention due to its capability to deal with multiple languages .", "however , the current multilingual translation paradigm often makes the model tend to preserve the general knowledge , but ignore the language - specific knowledge .", ...
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ACL
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
When primed with only a handful of training samples, very large, pretrained language models such as GPT-3 have shown competitive results when compared to fully-supervised, fine-tuned, large, pretrained language models. We demonstrate that the order in which the samples are provided can make the difference between near ...
ab3f84f0efd721490d5c6d817746aa1a
2,022
[ "when primed with only a handful of training samples , very large , pretrained language models such as gpt - 3 have shown competitive results when compared to fully - supervised , fine - tuned , large , pretrained language models .", "we demonstrate that the order in which the samples are provided can make the di...
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ACL
Detecting Propaganda Techniques in Memes
Propaganda can be defined as a form of communication that aims to influence the opinions or the actions of people towards a specific goal; this is achieved by means of well-defined rhetorical and psychological devices. Propaganda, in the form we know it today, can be dated back to the beginning of the 17th century. How...
c0135810b88c417d9cb1ee0a8c57a66e
2,021
[ "propaganda can be defined as a form of communication that aims to influence the opinions or the actions of people towards a specific goal ; this is achieved by means of well - defined rhetorical and psychological devices .", "propaganda , in the form we know it today , can be dated back to the beginning of the 1...
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ACL
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction
We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world’s languages. We evaluate our method on Parallel Bible Corpus+ (PBC+), a parallel corpus of 1593 languages. The key idea is to use Byte Pair Encodings (BPEs) as basic units ...
3faa4b0cef8856549b1d2841b8d1dab6
2,019
[ "we present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world ’ s languages .", "we evaluate our method on parallel bible corpus + ( pbc + ) , a parallel corpus of 1593 languages .", "the key idea is to use byte pair encoding...
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ACL
Learning to execute instructions in a Minecraft dialogue
The Minecraft Collaborative Building Task is a two-player game in which an Architect (A) instructs a Builder (B) to construct a target structure in a simulated Blocks World Environment. We define the subtask of predicting correct action sequences (block placements and removals) in a given game context, and show that ca...
3fa7c1ec3ba33c90238efe16ceac643e
2,020
[ "the minecraft collaborative building task is a two - player game in which an architect ( a ) instructs a builder ( b ) to construct a target structure in a simulated blocks world environment .", "we define the subtask of predicting correct action sequences ( block placements and removals ) in a given game contex...
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ACL
Fair and Argumentative Language Modeling for Computational Argumentation
Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy. In this paper, we address this research gap and conduct a thorough investigation of bias in argumentative language models. To this end...
0afdd636f33148fb6d3df7282ae2a4b2
2,022
[ "although much work in nlp has focused on measuring and mitigating stereotypical bias in semantic spaces , research addressing bias in computational argumentation is still in its infancy .", "in this paper , we address this research gap and conduct a thorough investigation of bias in argumentative language models...
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ACL
Does BERT Know that the IS-A Relation Is Transitive?
The success of a natural language processing (NLP) system on a task does not amount to fully understanding the complexity of the task, typified by many deep learning models. One such question is: can a black-box model make logically consistent predictions for transitive relations? Recent studies suggest that pre-traine...
c1a2ab72ed6282c879fee6124957fb86
2,022
[ "the success of a natural language processing ( nlp ) system on a task does not amount to fully understanding the complexity of the task , typified by many deep learning models .", "one such question is : can a black - box model make logically consistent predictions for transitive relations ?", "recent studies ...
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ACL
Zero-shot Text Classification via Reinforced Self-training
Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen classes is extremely hard. To tackle this problem, in this paper we propose a self-training based met...
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2,020
[ "zero - shot learning has been a tough problem since no labeled data is available for unseen classes during training , especially for classes with low similarity .", "in this situation , transferring from seen classes to unseen classes is extremely hard .", "to tackle this problem , in this paper we propose a s...
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ACL
Wetin dey with these comments? Modeling Sociolinguistic Factors Affecting Code-switching Behavior in Nigerian Online Discussions
Multilingual individuals code switch between languages as a part of a complex communication process. However, most computational studies have examined only one or a handful of contextual factors predictive of switching. Here, we examine Naija-English code switching in a rich contextual environment to understand the soc...
7caf4c98c8e3d3b531228a0b08630e7a
2,019
[ "multilingual individuals code switch between languages as a part of a complex communication process .", "however , most computational studies have examined only one or a handful of contextual factors predictive of switching .", "here , we examine naija - english code switching in a rich contextual environment ...
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ACL
DefSent: Sentence Embeddings using Definition Sentences
Sentence embedding methods using natural language inference (NLI) datasets have been successfully applied to various tasks. However, these methods are only available for limited languages due to relying heavily on the large NLI datasets. In this paper, we propose DefSent, a sentence embedding method that uses definitio...
265882d14c960818cf269cb172aa4439
2,021
[ "sentence embedding methods using natural language inference ( nli ) datasets have been successfully applied to various tasks .", "however , these methods are only available for limited languages due to relying heavily on the large nli datasets .", "in this paper , we propose defsent , a sentence embedding meth...
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ACL
Multi-grained Attention with Object-level Grounding for Visual Question Answering
Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question. Most approaches train attention models from a coarse-grained association between sentences and images, which tends to fail on small objects or uncommon concepts. To address this problem, this pape...
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2,019
[ "attention mechanisms are widely used in visual question answering ( vqa ) to search for visual clues related to the question .", "most approaches train attention models from a coarse - grained association between sentences and images , which tends to fail on small objects or uncommon concepts .", "to address t...
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ACL
Decoding Part-of-Speech from Human EEG Signals
This work explores techniques to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution with electroencephalography (EEG) during text reading. We first show that information about word length, frequency and word class is encoded by the brain at different post-stimulus latencies. We the...
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2,022
[ "this work explores techniques to predict part - of - speech ( pos ) tags from neural signals measured at millisecond resolution with electroencephalography ( eeg ) during text reading .", "we first show that information about word length , frequency and word class is encoded by the brain at different post - stim...
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ACL
Targeting the Benchmark: On Methodology in Current Natural Language Processing Research
It has become a common pattern in our field: One group introduces a language task, exemplified by a dataset, which they argue is challenging enough to serve as a benchmark. They also provide a baseline model for it, which then soon is improved upon by other groups. Often, research efforts then move on, and the pattern ...
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2,021
[ "it has become a common pattern in our field : one group introduces a language task , exemplified by a dataset , which they argue is challenging enough to serve as a benchmark .", "they also provide a baseline model for it , which then soon is improved upon by other groups .", "often , research efforts then mov...
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ACL
Deduplicating Training Data Makes Language Models Better
We find that existing language modeling datasets contain many near-duplicate examples and long repetitive substrings.As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data.We develop two tools that allow us to deduplicate training datasets—fo...
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2,022
[ "we find that existing language modeling datasets contain many near - duplicate examples and long repetitive substrings .", "as a result , over 1 % of the unprompted output of language models trained on these datasets is copied verbatim from the training data .", "we develop two tools that allow us to deduplica...
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ACL
Data Augmentation with Adversarial Training for Cross-Lingual NLI
Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages. In practice, however, we still face the problem of scarce labeled data, leading to subpar results. In this pape...
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2,021
[ "due to recent pretrained multilingual representation models , it has become feasible to exploit labeled data from one language to train a cross - lingual model that can then be applied to multiple new languages .", "in practice , however , we still face the problem of scarce labeled data , leading to subpar resu...
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ACL
Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing
We propose a general strategy named ‘divide, conquer and combine’ for multimodal fusion. Instead of directly fusing features at holistic level, we conduct fusion hierarchically so that both local and global interactions are considered for a comprehensive interpretation of multimodal embeddings. In the ‘divide’ and ‘con...
c9bf1501d1d4c25a80583686bf9b325e
2,019
[ "we propose a general strategy named ‘ divide , conquer and combine ’ for multimodal fusion .", "instead of directly fusing features at holistic level , we conduct fusion hierarchically so that both local and global interactions are considered for a comprehensive interpretation of multimodal embeddings .", "in ...
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ACL
DisSent: Learning Sentence Representations from Explicit Discourse Relations
Learning effective representations of sentences is one of the core missions of natural language understanding. Existing models either train on a vast amount of text, or require costly, manually curated sentence relation datasets. We show that with dependency parsing and rule-based rubrics, we can curate a high quality ...
8b4dfcef1a8d4341eabce3afaf5d5fbb
2,019
[ "learning effective representations of sentences is one of the core missions of natural language understanding .", "existing models either train on a vast amount of text , or require costly , manually curated sentence relation datasets .", "we show that with dependency parsing and rule - based rubrics , we can ...
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ACL
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References
Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses. Previous models are generally trained based on 1-to-1 mapping from an input query to its response, which actually ignores the nature of 1-...
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ACL
Fast and Accurate Non-Projective Dependency Tree Linearization
We propose a graph-based method to tackle the dependency tree linearization task. We formulate the task as a Traveling Salesman Problem (TSP), and use a biaffine attention model to calculate the edge costs. We facilitate the decoding by solving the TSP for each subtree and combining the solution into a projective tree....
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ACL
Latent Retrieval for Weakly Supervised Open Domain Question Answering
Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR....
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ACL
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction
The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages. Previous work has shown that morphological variation is an intractable challenge for the UBLI task, where the induced translation in failure case is usually morphologically related to ...
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ACL
Learning to Ask Unanswerable Questions for Machine Reading Comprehension
Machine reading comprehension with unanswerable questions is a challenging task. In this work, we propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. We introduce a pair-to...
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ACL
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclu...
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ACL
Simple Unsupervised Summarization by Contextual Matching
We propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the other that is specific to the target domain. We show that by using a product-of-experts criteria these are enough for maintaining continuous...
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ACL
Improving Neural Language Models by Segmenting, Attending, and Predicting the Future
Common language models typically predict the next word given the context. In this work, we propose a method that improves language modeling by learning to align the given context and the following phrase. The model does not require any linguistic annotation of phrase segmentation. Instead, we define syntactic heights a...
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2,019
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ACL
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking
This paper focuses on the end-to-end abstractive summarization of a single product review without supervision. We assume that a review can be described as a discourse tree, in which the summary is the root, and the child sentences explain their parent in detail. By recursively estimating a parent from its children, our...
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ACL
Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation
Accurate, automatic evaluation of machine translation is critical for system tuning, and evaluating progress in the field. We proposed a simple unsupervised metric, and additional supervised metrics which rely on contextual word embeddings to encode the translation and reference sentences. We find that these models riv...
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ACL
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
We introduce the first treebank for a romanized user-generated content variety of Algerian, a North-African Arabic dialect known for its frequent usage of code-switching. Made of 1500 sentences, fully annotated in morpho-syntax and Universal Dependency syntax, with full translation at both the word and the sentence lev...
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ACL
Joint Models for Answer Verification in Question Answering Systems
This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. Our work shows that a critical step to effectively exploiting an answer set regards modeling the inter...
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ACL
On the Importance of Diversity in Question Generation for QA
Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promo...
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ACL
Learning an Unreferenced Metric for Online Dialogue Evaluation
Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to develop automatic dialogue evaluation metrics, but most of them do not generalize to unseen datasets and/or need a human-generated reference resp...
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[ "evaluating the quality of a dialogue interaction between two agents is a difficult task , especially in open - domain chit - chat style dialogue .", "there have been recent efforts to develop automatic dialogue evaluation metrics , but most of them do not generalize to unseen datasets and / or need a human - gen...
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ACL
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases...
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ACL
Data Augmentation for Text Generation Without Any Augmented Data
Data augmentation is an effective way to improve the performance of many neural text generation models. However, current data augmentation methods need to define or choose proper data mapping functions that map the original samples into the augmented samples. In this work, we derive an objective to formulate the proble...
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ACL
Confusionset-guided Pointer Networks for Chinese Spelling Check
This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointe...
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ACL
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Switzerland, and China), five languages (English, German, French, Italian and Chinese) ...
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ACL
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks. However, PLMs may have risks in overfitting the pretraining tasks and data, which usually have gap with the target downstream tasks. Such gap may be difficult for existing PLM finetuning methods to overcome and l...
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[ "effectively finetuning pretrained language models ( plms ) is critical for their success in downstream tasks .", "however , plms may have risks in overfitting the pretraining tasks and data , which usually have gap with the target downstream tasks .", "such gap may be difficult for existing plm finetuning meth...
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ACL
Multilingual and Cross-Lingual Graded Lexical Entailment
Grounded in cognitive linguistics, graded lexical entailment (GR-LE) is concerned with fine-grained assertions regarding the directional hierarchical relationships between concepts on a continuous scale. In this paper, we present the first work on cross-lingual generalisation of GR-LE relation. Starting from HyperLex, ...
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2,019
[ "grounded in cognitive linguistics , graded lexical entailment ( gr - le ) is concerned with fine - grained assertions regarding the directional hierarchical relationships between concepts on a continuous scale .", "in this paper , we present the first work on cross - lingual generalisation of gr - le relation ."...
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ACL
Unsupervised Paraphrasing by Simulated Annealing
We propose UPSA, a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing. We model paraphrase generation as an optimization problem and propose a sophisticated objective function, involving semantic similarity, expression diversity, and language fluency of paraphrases. UPSA searches the sent...
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ACL
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data
We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, which is difficult to solve with existing QA methods due to their lack of scalability. To tackle this problem, we propose a novel end-to...
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ACL
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in document-level neural machine translation (NMT). Surprisingly, we find that the context encoder does not only ...
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ACL
Detecting Subevents using Discourse and Narrative Features
Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding. We present a supervised model for automatically identifying when one event is a subevent of another. Building on prior work, we introduce several novel features, in particular discourse and...
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[ "recognizing the internal structure of events is a challenging language processing task of great importance for text understanding .", "we present a supervised model for automatically identifying when one event is a subevent of another .", "building on prior work , we introduce several novel features , in parti...
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ACL
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultan...
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[ "it is commonly believed that knowledge of syntactic structure should improve language modeling .", "however , effectively and computationally efficiently incorporating syntactic structure into neural language models has been a challenging topic .", "in this paper , we make use of a multi - task objective , i ....
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ACL
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web
We show that margin-based bitext mining in a multilingual sentence space can be successfully scaled to operate on monolingual corpora of billions of sentences. We use 32 snapshots of a curated common crawl corpus (Wenzel et al, 2019) totaling 71 billion unique sentences. Using one unified approach for 90 languages, we ...
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ACL
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
In this paper, we study machine reading comprehension (MRC) on long texts: where a model takes as inputs a lengthy document and a query, extracts a text span from the document as an answer. State-of-the-art models (e.g., BERT) tend to use a stack of transformer layers that are pre-trained from a large number of unlabel...
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ACL
MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition
Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical inform...
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ACL
Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model
Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences. In this work, we present a computational approach to interpreting stereotypes in text through the Stere...
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ACL
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA
In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training. Specifically, we present two pre-training tasks, namely multilingual replaced token detection, and translation replaced token detection. Besides, we pretrain the model, named as XLM-E, on both multilingual and parallel corpora...
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[ "in this paper , we introduce electra - style tasks to cross - lingual language model pre - training .", "specifically , we present two pre - training tasks , namely multilingual replaced token detection , and translation replaced token detection .", "besides , we pretrain the model , named as xlm - e , on both...
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ACL
Learning Source Phrase Representations for Neural Machine Translation
The Transformer translation model (Vaswani et al., 2017) based on a multi-head attention mechanism can be computed effectively in parallel and has significantly pushed forward the performance of Neural Machine Translation (NMT). Though intuitively the attentional network can connect distant words via shorter network pa...
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ACL
Modeling Code-Switch Languages Using Bilingual Parallel Corpus
Language modeling is the technique to estimate the probability of a sequence of words. A bilingual language model is expected to model the sequential dependency for words across languages, which is difficult due to the inherent lack of suitable training data as well as diverse syntactic structure across languages. We p...
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ACL
Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset
We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP to assess how European solidarity discourses changed before and after the COVID-19 outbreak was declared a global pandemic. To this end, ...
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[ "we introduce the well - established social scientific concept of social solidarity and its contestation , anti - solidarity , as a new problem setting to supervised machine learning in nlp to assess how european solidarity discourses changed before and after the covid - 19 outbreak was declared a global pandemic ....
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ACL
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach. Most current neural sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. These methods learn to simplify ...
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ACL
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email’s content. In this paper, we propose and study the task of email subject line generation: automatically generating an email subject line from the email body. We create the first dataset for t...
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2,019
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ACL
Unsupervised Extractive Opinion Summarization Using Sparse Coding
Opinion summarization is the task of automatically generating summaries that encapsulate information expressed in multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic informatio...
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ACL
KNN-Contrastive Learning for Out-of-Domain Intent Classification
The Out-of-Domain (OOD) intent classification is a basic and challenging task for dialogue systems. Previous methods commonly restrict the region (in feature space) of In-domain (IND) intent features to be compact or simply-connected implicitly, which assumes no OOD intents reside, to learn discriminative semantic feat...
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ACL
Neural Machine Translation with Monolingual Translation Memory
Prior work has proved that Translation Memory (TM) can boost the performance of Neural Machine Translation (NMT). In contrast to existing work that uses bilingual corpus as TM and employs source-side similarity search for memory retrieval, we propose a new framework that uses monolingual memory and performs learnable m...
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ACL
(Re)construing Meaning in NLP
Human speakers have an extensive toolkit of ways to express themselves. In this paper, we engage with an idea largely absent from discussions of meaning in natural language understanding—namely, that the way something is expressed reflects different ways of conceptualizing or construing the information being conveyed. ...
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ACL
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modelin...
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[ "when translating natural language questions into sql queries to answer questions from a database , contemporary semantic parsing models struggle to generalize to unseen database schemas .", "the generalization challenge lies in ( a ) encoding the database relations in an accessible way for the semantic parser , ...
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ACL
How Multilingual is Multilingual BERT?
In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model fo...
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[ "in this paper , we show that multilingual bert ( m - bert ) , released by devlin et al . ( 2018 ) as a single language model pre - trained from monolingual corpora in 104 languages , is surprisingly good at zero - shot cross - lingual model transfer , in which task - specific annotations in one language are used t...
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ACL
Cross-Modal Discrete Representation Learning
In contrast to recent advances focusing on high-level representation learning across modalities, in this work we present a self-supervised learning framework that is able to learn a representation that captures finer levels of granularity across different modalities such as concepts or events represented by visual obje...
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[ "in contrast to recent advances focusing on high - level representation learning across modalities , in this work we present a self - supervised learning framework that is able to learn a representation that captures finer levels of granularity across different modalities such as concepts or events represented by v...
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ACL
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as...
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2,022
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ACL
Max-Margin Incremental CCG Parsing
Incremental syntactic parsing has been an active research area both for cognitive scientists trying to model human sentence processing and for NLP researchers attempting to combine incremental parsing with language modelling for ASR and MT. Most effort has been directed at designing the right transition mechanism, but ...
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ACL
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost
State-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words.To address this issue, we follow the principle of mimick-like models to generate vectors for unseen words, by learning the behavior of pre-trained embeddings using only the surface for...
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[ "state - of - the - art nlp systems represent inputs with word embeddings , but these are brittle when faced with out - of - vocabulary ( oov ) words .", "to address this issue , we follow the principle of mimick - like models to generate vectors for unseen words , by learning the behavior of pre - trained embedd...
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ACL
Transfer Capsule Network for Aspect Level Sentiment Classification
Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online w...
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ACL
Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration
Vision-language navigation (VLN) is a challenging task due to its large searching space in the environment. To address this problem, previous works have proposed some methods of fine-tuning a large model that pretrained on large-scale datasets. However, the conventional fine-tuning methods require extra human-labeled n...
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ACL
Large Scale Substitution-based Word Sense Induction
We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where each sense is associated with indicative words. Evaluation on...
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2,022
[ "we present a word - sense induction method based on pre - trained masked language models ( mlms ) , which can cheaply scale to large vocabularies and large corpora .", "the result is a corpus which is sense - tagged according to a corpus - derived sense inventory and where each sense is associated with indicativ...
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ACL
Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis is a fine-grained sentiment classification task. Recently, graph neural networks over dependency trees have been explored to explicitly model connections between aspects and opinion words. However, the improvement is limited due to the inaccuracy of the dependency parsing results and the...
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2,021
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ACL
Automatically Identifying Complaints in Social Media
Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experi...
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2,019
[ "complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation .", "automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the cust...
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ACL
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension
Multi-passage reading comprehension requires the ability to combine cross-passage information and reason over multiple passages to infer the answer. In this paper, we introduce the Dynamic Self-attention Network (DynSAN) for multi-passage reading comprehension task, which processes cross-passage information at token-le...
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2,019
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ACL
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go
Aligning with ACL 2022 special Theme on “Language Diversity: from Low Resource to Endangered Languages”, we discuss the major linguistic and sociopolitical challenges facing development of NLP technologies for African languages. Situating African languages in a typological framework, we discuss how the particulars of t...
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[ "aligning with acl 2022 special theme on “ language diversity : from low resource to endangered languages ” , we discuss the major linguistic and sociopolitical challenges facing development of nlp technologies for african languages .", "situating african languages in a typological framework , we discuss how the ...
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ACL
N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses
Spoken Language Understanding (SLU) systems parse speech into semantic structures like dialog acts and slots. This involves the use of an Automatic Speech Recognizer (ASR) to transcribe speech into multiple text alternatives (hypotheses). Transcription errors, ordinary in ASRs, impact downstream SLU performance negativ...
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ACL
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e.g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability. In this pap...
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[ "although contextualized embeddings generated from large - scale pre - trained models perform well in many tasks , traditional static embeddings ( e . g . , skip - gram , word2vec ) still play an important role in low - resource and lightweight settings due to their low computational cost , ease of deployment , and...
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ACL
Sense Embeddings are also Biased – Evaluating Social Biases in Static and Contextualised Sense Embeddings
Sense embedding learning methods learn different embeddings for the different senses of an ambiguous word. One sense of an ambiguous word might be socially biased while its other senses remain unbiased. In comparison to the numerous prior work evaluating the social biases in pretrained word embeddings, the biases in se...
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ACL
Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering
We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA). Motivated by evidence from psycholinguistics, we devise a set of linguistically-informed VQA tasks, which differ by the types of questions involved (Wh-questions and polar questions). We test...
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[ "we study the issue of catastrophic forgetting in the context of neural multimodal approaches to visual question answering ( vqa ) .", "motivated by evidence from psycholinguistics , we devise a set of linguistically - informed vqa tasks , which differ by the types of questions involved ( wh - questions and polar...
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ACL
Shortformer: Better Language Modeling using Shorter Inputs
Increasing the input length has been a driver of progress in language modeling with transformers. We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length. First, we show that initially training a model on short su...
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ACL
Empirical Linguistic Study of Sentence Embeddings
The purpose of the research is to answer the question whether linguistic information is retained in vector representations of sentences. We introduce a method of analysing the content of sentence embeddings based on universal probing tasks, along with the classification datasets for two contrasting languages. We perfor...
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2,019
[ "the purpose of the research is to answer the question whether linguistic information is retained in vector representations of sentences .", "we introduce a method of analysing the content of sentence embeddings based on universal probing tasks , along with the classification datasets for two contrasting language...
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ACL
Headed-Span-Based Projective Dependency Parsing
We propose a new method for projective dependency parsing based on headed spans. In a projective dependency tree, the largest subtree rooted at each word covers a contiguous sequence (i.e., a span) in the surface order. We call such a span marked by a root word headed span. A projective dependency tree can be represent...
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ACL
Generating Logical Forms from Graph Representations of Text and Entities
Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptual...
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2,019
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ACL
Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering
Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings. However, there are two potential drawbacks to these methods: one is that they focus too much on the superficial information of the frame-evoking verb ...
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ACL
Unsupervised Pivot Translation for Distant Languages
Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between similar languages (e.g., English-German translation), they perform poorly between distant languages, because unsupervised alignment does not w...
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ACL
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
In visual communication, text emphasis is used to increase the comprehension of written text to convey the author’s intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author’s intent and...
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ACL
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks
Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been developed to this end, state-of-the-art AI systems are brittle; failing to perform the underlying mathematical reasoning when they appear in...
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ACL
BPE-Dropout: Simple and Effective Subword Regularization
Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most frequent words intact while splitting the rare ones into multiple tokens. While multiple segmentations are possible even with...
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ACL
Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering
Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the convers...
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ACL
Negative Training for Neural Dialogue Response Generation
Although deep learning models have brought tremendous advancements to the field of open-domain dialogue response generation, recent research results have revealed that the trained models have undesirable generation behaviors, such as malicious responses and generic (boring) responses. In this work, we propose a framewo...
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[ "although deep learning models have brought tremendous advancements to the field of open - domain dialogue response generation , recent research results have revealed that the trained models have undesirable generation behaviors , such as malicious responses and generic ( boring ) responses .", "in this work , we...
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ACL
Neural News Recommendation with Topic-Aware News Representation
News recommendation can help users find interested news and alleviate information overload. The topic information of news is critical for learning accurate news and user representations for news recommendation. However, it is not considered in many existing news recommendation methods. In this paper, we propose a neura...
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ACL
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns
There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading. While promising results have been obtained through the use of transformer-based language models, little work has been undertaken to relate the performance of such models to general t...
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2,022
[ "there is a growing interest in the combined use of nlp and machine learning methods to predict gaze patterns during naturalistic reading .", "while promising results have been obtained through the use of transformer - based language models , little work has been undertaken to relate the performance of such model...
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ACL
Cross-media Structured Common Space for Multimedia Event Extraction
We introduce a new task, MultiMedia Event Extraction, which aims to extract events and their arguments from multimedia documents. We develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments. We propose a novel method, Weakly Aligned Structured Emb...
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[ "we introduce a new task , multimedia event extraction , which aims to extract events and their arguments from multimedia documents .", "we develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments .", "we propose a novel method , weakly al...
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ACL
SummScreen: A Dataset for Abstractive Screenplay Summarization
We introduce SummScreen, a summarization dataset comprised of pairs of TV series transcripts and human written recaps. The dataset provides a challenging testbed for abstractive summarization for several reasons. Plot details are often expressed indirectly in character dialogues and may be scattered across the entirety...
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ACL
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems
Conversational agents have come increasingly closer to human competence in open-domain dialogue settings; however, such models can reflect insensitive, hurtful, or entirely incoherent viewpoints that erode a user’s trust in the moral integrity of the system. Moral deviations are difficult to mitigate because moral judg...
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ACL
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification
Recent studies on neural networks with pre-trained weights (i.e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located. In this work, we propose a new approach, called OoMMix, to finding and regularizing the remainder of the space...
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[ "recent studies on neural networks with pre - trained weights ( i . e . , bert ) have mainly focused on a low - dimensional subspace , where the embedding vectors computed from input words ( or their contexts ) are located .", "in this work , we propose a new approach , called oommix , to finding and regularizing...
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ACL
Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units
Graphics Processing Units (GPUs) are commonly used to train and evaluate neural networks efficiently. While previous work in deep learning has focused on accelerating operations on dense matrices/tensors on GPUs, efforts have concentrated on operations involving sparse data structures. Operations using sparse structure...
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2,019
[ "graphics processing units ( gpus ) are commonly used to train and evaluate neural networks efficiently .", "while previous work in deep learning has focused on accelerating operations on dense matrices / tensors on gpus , efforts have concentrated on operations involving sparse data structures .", "operations ...
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ACL
More than Text: Multi-modal Chinese Word Segmentation
Chinese word segmentation (CWS) is undoubtedly an important basic task in natural language processing. Previous works only focus on the textual modality, but there are often audio and video utterances (such as news broadcast and face-to-face dialogues), where textual, acoustic and visual modalities normally exist. To t...
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2,021
[ "chinese word segmentation ( cws ) is undoubtedly an important basic task in natural language processing .", "previous works only focus on the textual modality , but there are often audio and video utterances ( such as news broadcast and face - to - face dialogues ) , where textual , acoustic and visual modalitie...
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ACL
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, ea...
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2,020
[ "interpretable rationales for model predictions play a critical role in practical applications .", "in this study , we develop models possessing interpretable inference process for structured prediction .", "specifically , we present a method of instance - based learning that learns similarities between spans ....
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ACL
KLEJ: Comprehensive Benchmark for Polish Language Understanding
In recent years, a series of Transformer-based models unlocked major improvements in general natural language understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which allow for a fair comparison of the proposed methods. However, such benchmarks are available on...
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[ "in recent years , a series of transformer - based models unlocked major improvements in general natural language understanding ( nlu ) tasks .", "such a fast pace of research would not be possible without general nlu benchmarks , which allow for a fair comparison of the proposed methods .", "however , such ben...
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