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ACL | Improving Compositional Generalization with Self-Training for Data-to-Text Generation | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). Such representations are compositional and it is costly to collect responses for all possible combinations of atomic meaning schemata, thereby necessitating few-shot generalization to novel MRs... | 0e1f77d06a4ec3904407d3add34de789 | 2,022 | [
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ACL | Chart-to-Text: A Large-Scale Benchmark for Chart Summarization | Charts are commonly used for exploring data and communicating insights. Generating natural language summaries from charts can be very helpful for people in inferring key insights that would otherwise require a lot of cognitive and perceptual efforts. We present Chart-to-text, a large-scale benchmark with two datasets a... | 488b039c90865391443422f8307b34bd | 2,022 | [
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ACL | Good-Enough Compositional Data Augmentation | We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that ... | 85b29d2b71ae289bd819ec0f8a81c676 | 2,020 | [
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ACL | EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation | Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superior performance against the conventional MNMT by constructing multi-way aligned corpus, i.e., aligning bilingual training examples from different language pairs when either their source or target sides are identical. However, since exactly identic... | 972c154b08b8939e122b61f6fca6208e | 2,022 | [
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ACL | Sparsifying Transformer Models with Trainable Representation Pooling | We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on the task-specific parts of an input. A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust train... | 757fde4711c714d9424d5538727cb36b | 2,022 | [
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ACL | There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory | Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous methods for knowledge selection only concentrate on the relevance between knowledge and dialogue context, ignoring the fact that age, hobby,... | 22916cd9b5011a5904c829f18465d8ce | 2,022 | [
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ACL | Learning Disentangled Textual Representations via Statistical Measures of Similarity | When working with textual data, a natural application of disentangled representations is the fair classification where the goal is to make predictions without being biased (or influenced) by sensible attributes that may be present in the data (e.g., age, gender or race). Dominant approaches to disentangle a sensitive a... | bbebafb05a942ae8b5b6cc90f338eced | 2,022 | [
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ACL | Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference? | Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions. In light of this steady progress, can we claim that the performance gap between the two is closed? Starting from this question, we present a systematic comparison be... | ef16783fbb91519879e436f385f2ac57 | 2,021 | [
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ACL | Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains | Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for ... | 55a11535399f5fff3dbd44a2d99debda | 2,019 | [
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ACL | Learning Morphosyntactic Analyzers from the Bible via Iterative Annotation Projection across 26 Languages | A large percentage of computational tools are concentrated in a very small subset of the planet’s languages. Compounding the issue, many languages lack the high-quality linguistic annotation necessary for the construction of such tools with current machine learning methods. In this paper, we address both issues simulta... | 32798e998558860ce35f93d5325a8937 | 2,019 | [
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ACL | Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study | In recent years, a flurry of morphological datasets had emerged, most notably UniMorph, aa multi-lingual repository of inflection tables. However, the flat structure of the current morphological annotation makes the treatment of some languages quirky, if not impossible, specifically in cases of polypersonal agreement. ... | db60d31dc48c2890dd7f0a7d4660ada1 | 2,022 | [
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ACL | Multi-Label Few-Shot Learning for Aspect Category Detection | Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD ta... | 5e7f79e809d6fb25d69278ae84f82291 | 2,021 | [
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ACL | Low-Resource Generation of Multi-hop Reasoning Questions | This paper focuses on generating multi-hop reasoning questions from the raw text in a low resource circumstance. Such questions have to be syntactically valid and need to logically correlate with the answers by deducing over multiple relations on several sentences in the text. Specifically, we first build a multi-hop g... | 2581799d112709641409e85a577ae98a | 2,020 | [
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ACL | Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning | Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale ... | a0240364cb9669934bb02f309ba58375 | 2,021 | [
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ACL | Factoring Statutory Reasoning as Language Understanding Challenges | Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address th... | 74979e5e7c74e98aacc7e2ac4cd86a2a | 2,021 | [
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ACL | RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models | Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification. Recent work has predominantly focused on measuring and mitigating bias in pretrai... | 026142804f9b9be79c2a855dc629cc87 | 2,021 | [
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ACL | The Referential Reader: A Recurrent Entity Network for Anaphora Resolution | We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory. The update operation implies coreference with the other mentions that are ... | 1295ab2c400aa998d33e8bdc9b8a7e5f | 2,019 | [
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ACL | TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition | Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction patterns for the task, which have not fully exploited the annotated relation signal. Therefore, we propo... | ab5c0a459c4bc2a8c15ce30443f38f96 | 2,020 | [
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ACL | Answer-level Calibration for Free-form Multiple Choice Question Answering | Pre-trained language models have recently shown that training on large corpora using the language modeling objective enables few-shot and zero-shot capabilities on a variety of NLP tasks, including commonsense reasoning tasks. This is achieved using text interactions with the model, usually by posing the task as a natu... | 654d1ad2a6a5c67668f92aa6df788c18 | 2,022 | [
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ACL | Explicit Utilization of General Knowledge in Machine Reading Comprehension | To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data... | 75b20cdd1db57e8f47809f1f8bbdf63d | 2,019 | [
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ACL | Semi-supervised Domain Adaptation for Dependency Parsing | During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data. However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and... | 4af46351f80e9c1555287f568a07a488 | 2,019 | [
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ACL | BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simpl... | 5001edc15e87534cbdcb8d9cfdb0435b | 2,020 | [
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ACL | Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds | Most NLP datasets are not annotated with protected attributes such as gender, making it difficult to measure classification bias using standard measures of fairness (e.g., equal opportunity). However, manually annotating a large dataset with a protected attribute is slow and expensive. Instead of annotating all the exa... | c1927a0b22cfbed963c0147a28cf5508 | 2,020 | [
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ACL | Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining | This work presents methods for learning cross-lingual sentence representations using paired or unpaired bilingual texts. We hypothesize that the cross-lingual alignment strategy is transferable, and therefore a model trained to align only two languages can encode multilingually more aligned representations. We thus int... | 8f3fe2ac08f08f9e8daaf9c01b6e676b | 2,022 | [
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ACL | ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification | Generating new events given context with correlated ones plays a crucial role in many event-centric reasoning tasks. Existing works either limit their scope to specific scenarios or overlook event-level correlations. In this paper, we propose to pre-train a general Correlation-aware context-to-Event Transformer (ClarET... | a21d9b9a55ade86c8fdbbeecfd962986 | 2,022 | [
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ACL | MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning | Generating multi-sentence descriptions for videos is one of the most challenging captioning tasks due to its high requirements for not only visual relevance but also discourse-based coherence across the sentences in the paragraph. Towards this goal, we propose a new approach called Memory-Augmented Recurrent Transforme... | 71f7e791dfed3f96b317a9fecca1ee7b | 2,020 | [
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ACL | Towards Visual Question Answering on Pathology Images | Pathology imaging is broadly used for identifying the causes and effects of diseases or injuries. Given a pathology image, being able to answer questions about the clinical findings contained in the image is very important for medical decision making. In this paper, we aim to develop a pathological visual question answ... | 500418b0211ec66f5057928be3eb6602 | 2,021 | [
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ACL | Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data | The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this w... | dba559012b70a3926bdf53834569aea8 | 2,021 | [
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ACL | RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English) | Most words are ambiguous—-i.e., they convey distinct meanings in different contexts—-and even the meanings of unambiguous words are context-dependent. Both phenomena present a challenge for NLP. Recently, the advent of contextualized word embeddings has led to success on tasks involving lexical ambiguity, such as Word ... | 81e2c09f2abe446bec57fef957aa9330 | 2,021 | [
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ACL | Adversarial Attention Modeling for Multi-dimensional Emotion Regression | In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text. Especially, to determine which words are valuable for a particular emotion dimension, an at... | 4be5df26b812c974f73657ca83c7bab2 | 2,019 | [
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ACL | Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases | Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting me... | 388bc1d05f2934d8ae6e9bd05851a03f | 2,021 | [
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ACL | Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference | Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering. Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown at inference time. T... | 022d8ced4d9e60e2ce4705911b329ca8 | 2,021 | [
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ACL | Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge | Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy. To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction... | 631c755da3afe135b33891784e4760eb | 2,019 | [
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ACL | Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation | We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM). We propose and test two methods for augmenting NMT training data with fuzzy TM matches. Tests on the DGT-TM data set for two language pa... | defc5af08d5a3cfd4b214fc463b49db5 | 2,019 | [
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ACL | Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages | Cross-lingual transfer learning with large multilingual pre-trained models can be an effective approach for low-resource languages with no labeled training data. Existing evaluations of zero-shot cross-lingual generalisability of large pre-trained models use datasets with English training data, and test data in a selec... | 623288ad9f22e7fc5633fc210a6d6c24 | 2,022 | [
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ACL | Improving Factual Consistency of Abstractive Summarization via Question Answering | A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application... | 555b19884a4fa9166b2ca40f9dfba4df | 2,021 | [
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ACL | On the Summarization of Consumer Health Questions | Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retr... | 91d803437bdd1c48c3c7c4c8a14fe939 | 2,019 | [
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ACL | Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving | With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems. However, these SOTA solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. As a result, the expression trees they produce... | 14e48134c310286fe9767eef4c2d121f | 2,021 | [
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ACL | What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts | According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people’s everyday life and career. We benchmark the novel task of automatically detecting those needs on short posts in English, by modelling it as a ternary classifica... | 805b44d2691eaa975a7bd6fa6cd59f3e | 2,021 | [
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ACL | Doctor Recommendation in Online Health Forums via Expertise Learning | Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task. To better help patients, this paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise. While most prior work in... | e8cf7e646314bb11794f5862d2c3ddc8 | 2,022 | [
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ACL | Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions | We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts. Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level... | 3927d5101af0718e01df8cddc104e0bf | 2,020 | [
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ACL | Generative Semantic Hashing Enhanced via Boltzmann Machines | Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint. For the tractability of training, existing generative-hashing methods mostly assume a factorized form for the posterior distribution, enforcing independence among the... | aa9ace2f3b147d6485ad865689250cf2 | 2,020 | [
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ACL | ELI5: Long Form Question Answering | We introduce the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum “Explain Like I’m Five” (ELI5) where an online community provides answers to questions which are comprehensible by... | 1c53670b2742efadf60c27db50017ffa | 2,019 | [
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ACL | AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER | Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages. While previous works have shown that unlabeled data in a target language can be used to improve cross-lingual model per... | 8ae622b7c4dc110d4376dae5766b9baf | 2,021 | [
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ACL | Text-Based Ideal Points | Ideal point models analyze lawmakers’ votes to quantify their political positions, or ideal points. But votes are not the only way to express a political position. Lawmakers also give speeches, release press statements, and post tweets. In this paper, we introduce the text-based ideal point model (TBIP), an unsupervise... | 65f42db473581c019ac5bb382b1e6f54 | 2,020 | [
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ACL | A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings | Word embedding models have gained a lot of traction in the Natural Language Processing community, however, they suffer from unintended demographic biases. Most approaches to evaluate these biases rely on vector space based metrics like the Word Embedding Association Test (WEAT). While these approaches offer great geome... | e23e6a719acf5c3465a6c4ddd05c48fe | 2,019 | [
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ACL | Speakers enhance contextually confusable words | Recent work has found evidence that natural languages are shaped by pressures for efficient communication — e.g. the more contextually predictable a word is, the fewer speech sounds or syllables it has (Piantadosi et al. 2011). Research on the degree to which speech and language are shaped by pressures for effective co... | 792cb0ed64039f0fa72222b9d502c441 | 2,020 | [
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ACL | JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages | Viable cross-lingual transfer critically depends on the availability of parallel texts. Shortage of such resources imposes a development and evaluation bottleneck in multilingual processing. We introduce JW300, a parallel corpus of over 300 languages with around 100 thousand parallel sentences per language pair on aver... | b22baed6686254e6c93463861f15d5fc | 2,019 | [
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ACL | Neural CRF Model for Sentence Alignment in Text Simplification | The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles. To evaluate and improve sentence alignment quality, we create two manually annotated sentence-aligned data... | fde70ccc4930dda6d028e0eaa3291415 | 2,020 | [
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ACL | Knowledge-aware Pronoun Coreference Resolution | Resolving pronoun coreference requires knowledge support, especially for particular domains (e.g., medicine). In this paper, we explore how to leverage different types of knowledge to better resolve pronoun coreference with a neural model. To ensure the generalization ability of our model, we directly incorporate knowl... | c1be83100aae6567ba3693124eb0cff9 | 2,019 | [
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ACL | Can Pre-trained Language Models Interpret Similes as Smart as Human? | Simile interpretation is a crucial task in natural language processing. Nowadays, pre-trained language models (PLMs) have achieved state-of-the-art performance on many tasks. However, it remains under-explored whether PLMs can interpret similes or not. In this paper, we investigate the ability of PLMs in simile interpr... | bdf8e4e10ddfc1834504021583aa31ee | 2,022 | [
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ACL | Combating Adversarial Misspellings with Robust Word Recognition | To combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier. Our word recognition models build upon the RNN semi-character architecture, introducing several new backoff strategies for handling rare and unseen words. Trained to recognize words corrupted by r... | 701fd5bb2c4572e0e9b13a89988df20e | 2,019 | [
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ACL | OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs | We study a conversational reasoning model that strategically traverses through a large-scale 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 15K... | d7e1317f477989c06b28b4ce8a83d792 | 2,019 | [
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ACL | How Do We Answer Complex Questions: Discourse Structure of Long-form Answers | Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. To better understand this complex and understudied task, we study the functional structure of long-form answers collected from three datasets, ELI5, WebGPT and Natural Questions. Our main go... | 22fa484d34fb8aac910530cc5e88b6ff | 2,022 | [
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ACL | VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena | We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V&L) models for their visio-linguistic grounding capabilities on specific linguistic phenomena. VALSE offers a suite of six tests covering various linguistic constructs. S... | c6021479efba6b07be4241e4f3580959 | 2,022 | [
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ACL | Parallel Data Augmentation for Formality Style Transfer | The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demons... | f1bf67186254ee7a9e88489fc0f59591 | 2,020 | [
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ACL | Towards User-Driven Neural Machine Translation | A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior... | bb191ab4746e31680dc9338ce64f50fd | 2,021 | [
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ACL | Learning to Deceive with Attention-Based Explanations | Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing. In addition to yielding gains in predictive accuracy, attention weights are often claimed to confer interpretability, purportedly useful both for providing insights to practitioners and for explaining why a mo... | 37acae72f18d810ee6281be3842d076e | 2,020 | [
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ACL | EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering | Natural language processing (NLP) often faces the problem of data diversity such as different domains, themes, styles, and so on. Therefore, a single language model (LM) is insufficient to learn all knowledge from diverse samples. To solve this problem, we firstly propose an autoencoding topic model with a mixture prio... | d5aa66ba29bdf7ef436042ad02c50356 | 2,021 | [
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ACL | MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes | We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history. When MemSum iteratively selects sentences into the summary, it considers a broad information set that wo... | ce92f5723e713a0f32ca1e63692aa402 | 2,022 | [
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ACL | Prix-LM: Pretraining for Multilingual Knowledge Base Construction | Knowledge bases (KBs) contain plenty of structured world and commonsense knowledge. As such, they often complement distributional text-based information and facilitate various downstream tasks. Since their manual construction is resource- and time-intensive, recent efforts have tried leveraging large pretrained languag... | 600dcf0e9015d3c46bbb2e0cb0b523ca | 2,022 | [
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ACL | On Continual Model Refinement in Out-of-Distribution Data Streams | Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrophic forgetting. However, existing continual learning (CL) problem setups cannot cover such a realistic and complex scenario. In response to ... | 75c2ce5e910eebd5b760b77354e5515e | 2,022 | [
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ACL | Differentiable Window for Dynamic Local Attention | We propose Differentiable Window, a new neural module and general purpose component for dynamic window selection. While universally applicable, we demonstrate a compelling use case of utilizing Differentiable Window to improve standard attention modules by enabling more focused attentions over the input regions. We pro... | 29e797bb0cc54d8b2093b5281d8084d6 | 2,020 | [
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ACL | De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation | Logical table-to-text generation aims to automatically generate fluent and logically faithful text from tables. The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text. The underlying reason may be that deep learning models often capture surface-leve... | 091d55fbdadcf76fd4d0d399f005301c | 2,021 | [
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ACL | Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction | Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems. To address this, lexically constrained NMT explores various methods to ensure pre-specified words and phrases appear in the translation output. However, in many cases, those methods are... | ed4f3268dcc0488466146a39ceddacff | 2,021 | [
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ACL | Gender bias amplification during Speed-Quality optimization in Neural Machine Translation | Is bias amplified when neural machine translation (NMT) models are optimized for speed and evaluated on generic test sets using BLEU? We investigate architectures and techniques commonly used to speed up decoding in Transformer-based models, such as greedy search, quantization, average attention networks (AANs) and sha... | c59a6ebe7a58b40677ca0f1d980e0905 | 2,021 | [
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ACL | ChartDialogs: Plotting from Natural Language Instructions | This paper presents the problem of conversational plotting agents that carry out plotting actions from natural language instructions. To facilitate the development of such agents, we introduce ChartDialogs, a new multi-turn dialog dataset, covering a popular plotting library, matplotlib. The dataset contains over 15,00... | 881873fef50f5e749c78601825cd5865 | 2,020 | [
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ACL | OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres | SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. This paper provides a dataset and comprehensive evaluation showing that the lates... | abadae1583efd77c478972fb136f7285 | 2,021 | [
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ACL | DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions | Short textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question answering. However, generating entity descriptions, especially for new and long-tail entities, can be challenging since relevant... | 265e1fd74a1adf93bdde7b65ffa8ee6e | 2,021 | [
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ACL | ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse Paraphrasing | Recent research considers few-shot intent detection as a meta-learning problem: the model is learning to learn from a consecutive set of small tasks named episodes. In this work, we propose ProtAugment, a meta-learning algorithm for short texts classification (the intent detection task). ProtAugment is a novel extensio... | 166bdde6202d5b0e3f3e755a92a0047c | 2,021 | [
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ACL | Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates | Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that influence both potential causes and potential effects. Unmeasured or latent confounders can bias causal estimates, and this has motivated inter... | c5f00f11751901360cc73337db8f984f | 2,020 | [
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ACL | Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good | Developing intelligent persuasive conversational agents to change people’s opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human ... | 3c3f93160649aeecb434991ac0c76299 | 2,019 | [
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ACL | DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation | Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controllable generation approach in order to deal with this domain adaptation (DA) challenge. Given an input text example, our DoCoGen algorithm gene... | 6de2772928a7d46673fe6a31cc7160a3 | 2,022 | [
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ACL | Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification | Hierarchical Text Classification (HTC) is a challenging task that categorizes a textual description within a taxonomic hierarchy. Most of the existing methods focus on modeling the text. Recently, researchers attempt to model the class representations with some resources (e.g., external dictionaries). However, the conc... | 213a0b033c2822a602328e3fe20bc126 | 2,021 | [
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ACL | Clinical Concept Linking with Contextualized Neural Representations | In traditional approaches to entity linking, linking decisions are based on three sources of information – the similarity of the mention string to an entity’s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contex... | 44ff5a904349d8114f70fd0b91e6e7b7 | 2,020 | [
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ACL | Simulating Bandit Learning from User Feedback for Extractive Question Answering | We study learning from user feedback for extractive question answering by simulating feedback using supervised data. We cast the problem as contextual bandit learning, and analyze the characteristics of several learning scenarios with focus on reducing data annotation. We show that systems initially trained on few exam... | d6f332dfa5877a2b51c52244b6a7433e | 2,022 | [
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ACL | Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition | We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the nois... | 599779b3902c0ba8bde34d709d8b8529 | 2,019 | [
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ACL | Programming in Natural Language with fuSE: Synthesizing Methods from Spoken Utterances Using Deep Natural Language Understanding | The key to effortless end-user programming is natural language. We examine how to teach intelligent systems new functions, expressed in natural language. As a first step, we collected 3168 samples of teaching efforts in plain English. Then we built fuSE, a novel system that translates English function descriptions into... | eee2471900b04ff861bd2e6494b99d6b | 2,020 | [
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ACL | Selecting Informative Contexts Improves Language Model Fine-tuning | Language model fine-tuning is essential for modern natural language processing, but is computationally expensive and time-consuming. Further, the effectiveness of fine-tuning is limited by the inclusion of training examples that negatively affect performance. Here we present a general fine-tuning method that we call in... | 5f5c31f8d2067790894ef8abd172a072 | 2,021 | [
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ACL | Improving Adversarial Text Generation by Modeling the Distant Future | Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and pres... | c5439dbdc2773edf85ecb2a330281708 | 2,020 | [
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ACL | CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning | Approaches to Grounded Language Learning are commonly focused on a single task-based final performance measure which may not depend on desirable properties of the learned hidden representations, such as their ability to predict object attributes or generalize to unseen situations. To remedy this, we present GroLLA, an ... | 66826acfebf0fcc728292d40fe601ac3 | 2,020 | [
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ACL | Revisiting Unsupervised Relation Extraction | Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstr... | 7346b2ab2fce5fb947f826b9a3bde05b | 2,020 | [
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ACL | Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation | Recently, token-level adaptive training has achieved promising improvement in machine translation, where the cross-entropy loss function is adjusted by assigning different training weights to different tokens, in order to alleviate the token imbalance problem. However, previous approaches only use static word frequency... | 929708dd97384be3b42e3a54898960e5 | 2,021 | [
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ACL | Multi-Relational Script Learning for Discourse Relations | Modeling script knowledge can be useful for a wide range of NLP tasks. Current statistical script learning approaches embed the events, such that their relationships are indicated by their similarity in the embedding. While intuitive, these approaches fall short of representing nuanced relations, needed for downstream ... | 2372e3c590b22c41de57d77a464646c2 | 2,019 | [
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ACL | Voxel-informed Language Grounding | Natural language applied to natural 2D images describes a fundamentally 3D world. We present the Voxel-informed Language Grounder (VLG), a language grounding model that leverages 3D geometric information in the form of voxel maps derived from the visual input using a volumetric reconstruction model. We show that VLG si... | c360eaab5798739f3374ca75b1c37816 | 2,022 | [
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ACL | Learning Robust Models for e-Commerce Product Search | Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is ... | 17c1dbc7030c36334a95cd12a3c9cb18 | 2,020 | [
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ACL | StRE: Self Attentive Edit Quality Prediction in Wikipedia | Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects. This creates an immense scope, in the field of natural language processing toward developing automated tools for content moderation and review. In this paper we propose... | 04f700322912e300c5fae66b8bbb361e | 2,019 | [
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ACL | mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models | Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities.However, existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks.In this study, we explore th... | a4b07c349961d027b0a53068da51a263 | 2,022 | [
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ACL | Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models | Recently, a variety of unsupervised methods have been proposed that map pre-trained word embeddings of different languages into the same space without any parallel data. These methods aim to find a linear transformation based on the assumption that monolingual word embeddings are approximately isomorphic between langua... | 8a0149398752ee5d7e39e35dd76625f8 | 2,019 | [
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ACL | WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge | In this paper, we present the first comprehensive categorization of essential commonsense knowledge for answering the Winograd Schema Challenge (WSC). For each of the questions, we invite annotators to first provide reasons for making correct decisions and then categorize them into six major knowledge categories. By do... | 2a611b3939cbd4a20528c8ec8a393fa8 | 2,020 | [
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