paper_id stringlengths 15 18 | title stringlengths 22 143 | paper_url stringlengths 41 44 | authors listlengths 1 16 | abstract large_stringlengths 439 1.79k | anthology_id stringlengths 15 18 | doi stringlengths 27 30 | award stringclasses 3
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2021.acl-long.501 | DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation | https://aclanthology.org/2021.acl-long.501/ | [
"Xinyu Hua",
"Ashwin Sreevatsa",
"Lu Wang"
] | We study the task of long-form opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiring efficient content planning. Second, diverse types of information are needed to guide the generator to cover both subjective and objective... | 2021.acl-long.501 | 10.18653/v1/2021.acl-long.501 | null | 2106.00791 | title_snapshot |
2021.acl-long.502 | Controllable Open-ended Question Generation with A New Question Type Ontology | https://aclanthology.org/2021.acl-long.502/ | [
"Shuyang Cao",
"Lu Wang"
] | We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which differentiates the nuanced nature of questions better than widely used question words. A new dataset with 4,959 questions is labeled based on the... | 2021.acl-long.502 | 10.18653/v1/2021.acl-long.502 | null | 2107.00152 | title_snapshot |
2021.acl-long.503 | BERTGen: Multi-task Generation through BERT | https://aclanthology.org/2021.acl-long.503/ | [
"Faidon Mitzalis",
"Ozan Caglayan",
"Pranava Madhyastha",
"Lucia Specia"
] | We present BERTGen, a novel, generative, decoder-only model which extends BERT by fusing multimodal and multilingual pre-trained models VL-BERT and M-BERT, respectively. BERTGen is auto-regressively trained for language generation tasks, namely image captioning, machine translation and multimodal machine translation, u... | 2021.acl-long.503 | 10.18653/v1/2021.acl-long.503 | null | 2106.03484 | title_snapshot |
2021.acl-long.504 | Selective Knowledge Distillation for Neural Machine Translation | https://aclanthology.org/2021.acl-long.504/ | [
"Fusheng Wang",
"Jianhao Yan",
"Fandong Meng",
"Jie Zhou"
] | Neural Machine Translation (NMT) models achieve state-of-the-art performance on many translation benchmarks. As an active research field in NMT, knowledge distillation is widely applied to enhance the model’s performance by transferring teacher model’s knowledge on each training sample. However, previous work rarely di... | 2021.acl-long.504 | 10.18653/v1/2021.acl-long.504 | null | 2105.12967 | title_snapshot |
2021.acl-long.505 | Measuring and Increasing Context Usage in Context-Aware Machine Translation | https://aclanthology.org/2021.acl-long.505/ | [
"Patrick Fernandes",
"Kayo Yin",
"Graham Neubig",
"André F. T. Martins"
] | Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context, context from sentences other than those currently being translated. However, while many current methods present model architectures that theoretically can use this extra context, it is often ... | 2021.acl-long.505 | 10.18653/v1/2021.acl-long.505 | null | 2105.03482 | title_snapshot |
2021.acl-long.506 | Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring | https://aclanthology.org/2021.acl-long.506/ | [
"Aitor Ormazabal",
"Mikel Artetxe",
"Aitor Soroa",
"Gorka Labaka",
"Eneko Agirre"
] | Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes departures from this a... | 2021.acl-long.506 | 10.18653/v1/2021.acl-long.506 | null | 2012.15715 | title_snapshot |
2021.acl-long.507 | CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web | https://aclanthology.org/2021.acl-long.507/ | [
"Holger Schwenk",
"Guillaume Wenzek",
"Sergey Edunov",
"Edouard Grave",
"Armand Joulin",
"Angela Fan"
] | 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 ... | 2021.acl-long.507 | 10.18653/v1/2021.acl-long.507 | null | 1911.04944 | title_snapshot |
2021.acl-long.508 | Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search | https://aclanthology.org/2021.acl-long.508/ | [
"Gyuwan Kim",
"Kyunghyun Cho"
] | Despite transformers’ impressive accuracy, their computational cost is often prohibitive to use with limited computational resources. Most previous approaches to improve inference efficiency require a separate model for each possible computational budget. In this paper, we extend PoWER-BERT (Goyal et al., 2020) and pro... | 2021.acl-long.508 | 10.18653/v1/2021.acl-long.508 | null | 2010.07003 | title_snapshot |
2021.acl-long.509 | GhostBERT: Generate More Features with Cheap Operations for BERT | https://aclanthology.org/2021.acl-long.509/ | [
"Zhiqi Huang",
"Lu Hou",
"Lifeng Shang",
"Xin Jiang",
"Xiao Chen",
"Qun Liu"
] | Transformer-based pre-trained language models like BERT, though powerful in many tasks, are expensive in both memory and computation, due to their large number of parameters. Previous works show that some parameters in these models can be pruned away without severe accuracy drop. However, these redundant features contr... | 2021.acl-long.509 | 10.18653/v1/2021.acl-long.509 | null | null | null |
2021.acl-long.510 | Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization | https://aclanthology.org/2021.acl-long.510/ | [
"Chen Liang",
"Simiao Zuo",
"Minshuo Chen",
"Haoming Jiang",
"Xiaodong Liu",
"Pengcheng He",
"Tuo Zhao",
"Weizhu Chen"
] | The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ”lottery tickets”, and training a certain collection of them (i.e., a subnetwork) can match the performance of the full model. In this paper, we study such a collection of tickets, which is referred to as ”winning tickets”, in extremel... | 2021.acl-long.510 | 10.18653/v1/2021.acl-long.510 | null | 2105.12002 | title_snapshot |
2021.acl-long.511 | A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations | https://aclanthology.org/2021.acl-long.511/ | [
"Pierre Colombo",
"Pablo Piantanida",
"Chloé Clavel"
] | Learning disentangled representations of textual data is essential for many natural language tasks such as fair classification, style transfer and sentence generation, among others. The existent dominant approaches in the context of text data either rely on training an adversary (discriminator) that aims at making attr... | 2021.acl-long.511 | 10.18653/v1/2021.acl-long.511 | null | 2105.02685 | title_snapshot |
2021.acl-long.512 | Determinantal Beam Search | https://aclanthology.org/2021.acl-long.512/ | [
"Clara Meister",
"Martina Forster",
"Ryan Cotterell"
] | Beam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does not reflect interactions between candidates. Empirically, this leads to sets often exhibiting high overlap, e.g., strings may ... | 2021.acl-long.512 | 10.18653/v1/2021.acl-long.512 | null | 2106.07400 | title_snapshot |
2021.acl-long.513 | Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning | https://aclanthology.org/2021.acl-long.513/ | [
"Shuoran Jiang",
"Qingcai Chen",
"Xin Liu",
"Baotian Hu",
"Lisai Zhang"
] | Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN may miss some important non-consecutive dependencies. In this study, we define the spectral... | 2021.acl-long.513 | 10.18653/v1/2021.acl-long.513 | null | 2106.05221 | title_snapshot |
2021.acl-long.514 | Accelerating Text Communication via Abbreviated Sentence Input | https://aclanthology.org/2021.acl-long.514/ | [
"Jiban Adhikary",
"Jamie Berger",
"Keith Vertanen"
] | Typing every character in a text message may require more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce a user’s physical input effort. This can be particularly important for people with motor impairments. In a large crowdsourced study, we found worker... | 2021.acl-long.514 | 10.18653/v1/2021.acl-long.514 | null | null | null |
2021.acl-long.515 | Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates | https://aclanthology.org/2021.acl-long.515/ | [
"Yuqing Xie",
"Yi-An Lai",
"Yuanjun Xiong",
"Yi Zhang",
"Stefano Soatto"
] | Behavior of deep neural networks can be inconsistent between different versions. Regressions during model update are a common cause of concern that often over-weigh the benefits in accuracy or efficiency gain. This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. Using neg... | 2021.acl-long.515 | 10.18653/v1/2021.acl-long.515 | null | 2105.03048 | title_snapshot |
2021.acl-long.516 | Detecting Propaganda Techniques in Memes | https://aclanthology.org/2021.acl-long.516/ | [
"Dimitar Dimitrov",
"Bishr Bin Ali",
"Shaden Shaar",
"Firoj Alam",
"Fabrizio Silvestri",
"Hamed Firooz",
"Preslav Nakov",
"Giovanni Da San Martino"
] | 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... | 2021.acl-long.516 | 10.18653/v1/2021.acl-long.516 | null | 2109.08013 | title_snapshot |
2021.acl-long.517 | On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study | https://aclanthology.org/2021.acl-long.517/ | [
"Divyansh Kaushik",
"Douwe Kiela",
"Zachary C. Lipton",
"Wen-tau Yih"
] | In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC’s ... | 2021.acl-long.517 | 10.18653/v1/2021.acl-long.517 | null | 2106.00872 | title_snapshot |
2021.acl-long.518 | Learning Dense Representations of Phrases at Scale | https://aclanthology.org/2021.acl-long.518/ | [
"Jinhyuk Lee",
"Mujeen Sung",
"Jaewoo Kang",
"Danqi Chen"
] | Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019). However, current phrase retrieval models heavily depend on sparse representations and still underperform retriever-reader approaches. In this work, w... | 2021.acl-long.518 | 10.18653/v1/2021.acl-long.518 | null | 2012.12624 | title_snapshot |
2021.acl-long.519 | End-to-End Training of Neural Retrievers for Open-Domain Question Answering | https://aclanthology.org/2021.acl-long.519/ | [
"Devendra Sachan",
"Mostofa Patwary",
"Mohammad Shoeybi",
"Neel Kant",
"Wei Ping",
"William L. Hamilton",
"Bryan Catanzaro"
] | Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used most effectively for neural retrievers. In this work, we systematically study retriever pre-trai... | 2021.acl-long.519 | 10.18653/v1/2021.acl-long.519 | null | 2101.00408 | title_snapshot |
2021.acl-long.520 | Question Answering Over Temporal Knowledge Graphs | https://aclanthology.org/2021.acl-long.520/ | [
"Apoorv Saxena",
"Soumen Chakrabarti",
"Partha Talukdar"
] | Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack ... | 2021.acl-long.520 | 10.18653/v1/2021.acl-long.520 | null | 2106.01515 | title_snapshot |
2021.acl-long.521 | Language Model Augmented Relevance Score | https://aclanthology.org/2021.acl-long.521/ | [
"Ruibo Liu",
"Jason Wei",
"Soroush Vosoughi"
] | Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknesses in prior metrics such as BLEU and ROUGE, which rely on n-gram matching. These newer methods, however, are still limited in that they do ... | 2021.acl-long.521 | 10.18653/v1/2021.acl-long.521 | null | 2108.08485 | title_snapshot |
2021.acl-long.522 | DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts | https://aclanthology.org/2021.acl-long.522/ | [
"Alisa Liu",
"Maarten Sap",
"Ximing Lu",
"Swabha Swayamdipta",
"Chandra Bhagavatula",
"Noah A. Smith",
"Yejin Choi"
] | Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language model with “expert” LMs and/or “anti-expert” LMs in a product of e... | 2021.acl-long.522 | 10.18653/v1/2021.acl-long.522 | null | 2105.03023 | title_snapshot |
2021.acl-long.523 | Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models | https://aclanthology.org/2021.acl-long.523/ | [
"Tongshuang Wu",
"Marco Tulio Ribeiro",
"Jeffrey Heer",
"Daniel Weld"
] | While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactua... | 2021.acl-long.523 | 10.18653/v1/2021.acl-long.523 | null | 2101.00288 | title_snapshot |
2021.acl-long.524 | Metaphor Generation with Conceptual Mappings | https://aclanthology.org/2021.acl-long.524/ | [
"Kevin Stowe",
"Tuhin Chakrabarty",
"Nanyun Peng",
"Smaranda Muresan",
"Iryna Gurevych"
] | Generating metaphors is a difficult task as it requires understanding nuanced relationships between abstract concepts. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Guided by conceptual metaphor theory, we propose to control the generation process by enc... | 2021.acl-long.524 | 10.18653/v1/2021.acl-long.524 | null | 2106.01228 | title_snapshot |
2021.acl-long.525 | Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols | https://aclanthology.org/2021.acl-long.525/ | [
"Chaitanya Kulkarni",
"Jany Chan",
"Eric Fosler-Lussier",
"Raghu Machiraju"
] | Wet laboratory protocols (WLPs) are critical for conveying reproducible procedures in biological research. They are composed of instructions written in natural language describing the step-wise processing of materials by specific actions. This process flow description for reagents and materials synthesis in WLPs can be... | 2021.acl-long.525 | 10.18653/v1/2021.acl-long.525 | null | null | null |
2021.acl-long.526 | Multimodal Multi-Speaker Merger & Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines | https://aclanthology.org/2021.acl-long.526/ | [
"Ramit Sawhney",
"Mihir Goyal",
"Prakhar Goel",
"Puneet Mathur",
"Rajiv Ratn Shah"
] | Risk prediction is an essential task in financial markets. Merger and Acquisition (M&A) calls provide key insights into the claims made by company executives about the restructuring of the financial firms. Extracting vocal and textual cues from M&A calls can help model the risk associated with such financial activities... | 2021.acl-long.526 | 10.18653/v1/2021.acl-long.526 | null | null | null |
2021.acl-long.527 | Mid-Air Hand Gestures for Post-Editing of Machine Translation | https://aclanthology.org/2021.acl-long.527/ | [
"Rashad Albo Jamara",
"Nico Herbig",
"Antonio Krüger",
"Josef van Genabith"
] | To translate large volumes of text in a globally connected world, more and more translators are integrating machine translation (MT) and post-editing (PE) into their translation workflows to generate publishable quality translations. While this process has been shown to save time and reduce errors, the task of translat... | 2021.acl-long.527 | 10.18653/v1/2021.acl-long.527 | null | null | null |
2021.acl-long.528 | Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning | https://aclanthology.org/2021.acl-long.528/ | [
"Pan Lu",
"Ran Gong",
"Shibiao Jiang",
"Liang Qiu",
"Siyuan Huang",
"Xiaodan Liang",
"Song-Chun Zhu"
] | 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 ... | 2021.acl-long.528 | 10.18653/v1/2021.acl-long.528 | null | 2105.04165 | title_snapshot |
2021.acl-long.529 | Joint Verification and Reranking for Open Fact Checking Over Tables | https://aclanthology.org/2021.acl-long.529/ | [
"Michael Sejr Schlichtkrull",
"Vladimir Karpukhin",
"Barlas Oguz",
"Mike Lewis",
"Wen-tau Yih",
"Sebastian Riedel"
] | Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on textual data, and the few recent inquiries into structured data have been for the closed-domain setting where appropriate evidence for each ... | 2021.acl-long.529 | 10.18653/v1/2021.acl-long.529 | null | 2012.15115 | title_snapshot |
2021.acl-long.530 | Evaluation of Thematic Coherence in Microblogs | https://aclanthology.org/2021.acl-long.530/ | [
"Iman Munire Bilal",
"Bo Wang",
"Maria Liakata",
"Rob Procter",
"Adam Tsakalidis"
] | Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time ... | 2021.acl-long.530 | 10.18653/v1/2021.acl-long.530 | null | 2106.15971 | title_snapshot |
2021.acl-long.531 | Neural semi-Markov CRF for Monolingual Word Alignment | https://aclanthology.org/2021.acl-long.531/ | [
"Wuwei Lan",
"Chao Jiang",
"Wei Xu"
] | Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment mo... | 2021.acl-long.531 | 10.18653/v1/2021.acl-long.531 | null | 2106.02569 | title_snapshot |
2021.acl-long.532 | Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies | https://aclanthology.org/2021.acl-long.532/ | [
"Mukund Srinath",
"Shomir Wilson",
"C Lee Giles"
] | Organisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since understanding them requires a significant investment of time and effort. Natural language processing has been ... | 2021.acl-long.532 | 10.18653/v1/2021.acl-long.532 | null | 2004.11131 | title_snapshot |
2021.acl-long.533 | The statistical advantage of automatic NLG metrics at the system level | https://aclanthology.org/2021.acl-long.533/ | [
"Johnny Wei",
"Robin Jia"
] | Estimating the expected output quality of generation systems is central to NLG. This paper qualifies the notion that automatic metrics are not as good as humans in estimating system-level quality. Statistically, humans are unbiased, high variance estimators, while metrics are biased, low variance estimators. We compare... | 2021.acl-long.533 | 10.18653/v1/2021.acl-long.533 | null | 2105.12437 | title_snapshot |
2021.acl-long.534 | Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion | https://aclanthology.org/2021.acl-long.534/ | [
"Yixin Cao",
"Xiang Ji",
"Xin Lv",
"Juanzi Li",
"Yonggang Wen",
"Hanwang Zhang"
] | We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. To ensure it, we propose to utilize rule-guided train/test generation, instead of... | 2021.acl-long.534 | 10.18653/v1/2021.acl-long.534 | null | 2108.01387 | title_snapshot |
2021.acl-long.535 | ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining | https://aclanthology.org/2021.acl-long.535/ | [
"Alexander Fabbri",
"Faiaz Rahman",
"Imad Rizvi",
"Borui Wang",
"Haoran Li",
"Yashar Mehdad",
"Dragomir Radev"
] | While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design a... | 2021.acl-long.535 | 10.18653/v1/2021.acl-long.535 | null | 2106.00829 | title_snapshot |
2021.acl-long.536 | Improving Factual Consistency of Abstractive Summarization via Question Answering | https://aclanthology.org/2021.acl-long.536/ | [
"Feng Nan",
"Cicero Nogueira dos Santos",
"Henghui Zhu",
"Patrick Ng",
"Kathleen McKeown",
"Ramesh Nallapati",
"Dejiao Zhang",
"Zhiguo Wang",
"Andrew O. Arnold",
"Bing Xiang"
] | 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... | 2021.acl-long.536 | 10.18653/v1/2021.acl-long.536 | null | 2105.04623 | title_snapshot |
2021.acl-long.537 | EmailSum: Abstractive Email Thread Summarization | https://aclanthology.org/2021.acl-long.537/ | [
"Shiyue Zhang",
"Asli Celikyilmaz",
"Jianfeng Gao",
"Mohit Bansal"
] | Recent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the long text to quickly catch up with the decisions made and thus improve our work or communication efficiency. To spur research in thread summar... | 2021.acl-long.537 | 10.18653/v1/2021.acl-long.537 | null | 2107.14691 | title_snapshot |
2021.acl-long.538 | Cross-Lingual Abstractive Summarization with Limited Parallel Resources | https://aclanthology.org/2021.acl-long.538/ | [
"Yu Bai",
"Yang Gao",
"Heyan Huang"
] | Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. Howev... | 2021.acl-long.538 | 10.18653/v1/2021.acl-long.538 | null | 2105.13648 | title_snapshot |
2021.acl-long.539 | Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution | https://aclanthology.org/2021.acl-long.539/ | [
"Jiacheng Xu",
"Greg Durrett"
] | Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from. We propose a two-step method to interpret summarization model decisions. We first analyze the model’s behavior by ablating the full model to cat... | 2021.acl-long.539 | 10.18653/v1/2021.acl-long.539 | null | 2106.01518 | title_snapshot |
2021.acl-long.540 | Learning Prototypical Functions for Physical Artifacts | https://aclanthology.org/2021.acl-long.540/ | [
"Tianyu Jiang",
"Ellen Riloff"
] | Humans create things for a reason. Ancient people created spears for hunting, knives for cutting meat, pots for preparing food, etc. The prototypical function of a physical artifact is a kind of commonsense knowledge that we rely on to understand natural language. For example, if someone says “She borrowed the book” th... | 2021.acl-long.540 | 10.18653/v1/2021.acl-long.540 | null | null | null |
2021.acl-long.541 | Verb Knowledge Injection for Multilingual Event Processing | https://aclanthology.org/2021.acl-long.541/ | [
"Olga Majewska",
"Ivan Vulić",
"Goran Glavaš",
"Edoardo Maria Ponti",
"Anna Korhonen"
] | Linguistic probing of pretrained Transformer-based language models (LMs) revealed that they encode a range of syntactic and semantic properties of a language. However, they are still prone to fall back on superficial cues and simple heuristics to solve downstream tasks, rather than leverage deeper linguistic informatio... | 2021.acl-long.541 | 10.18653/v1/2021.acl-long.541 | null | 2012.15421 | title_snapshot |
2021.acl-long.542 | Dynamic Contextualized Word Embeddings | https://aclanthology.org/2021.acl-long.542/ | [
"Valentin Hofmann",
"Janet Pierrehumbert",
"Hinrich Schütze"
] | Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function... | 2021.acl-long.542 | 10.18653/v1/2021.acl-long.542 | null | 2010.12684 | title_snapshot |
2021.acl-long.543 | Lexical Semantic Change Discovery | https://aclanthology.org/2021.acl-long.543/ | [
"Sinan Kurtyigit",
"Maike Park",
"Dominik Schlechtweg",
"Jonas Kuhn",
"Sabine Schulte im Walde"
] | While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full... | 2021.acl-long.543 | 10.18653/v1/2021.acl-long.543 | null | 2106.03111 | title_snapshot |
2021.acl-long.544 | The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity | https://aclanthology.org/2021.acl-long.544/ | [
"David Gros",
"Yu Li",
"Zhou Yu"
] | Humans are increasingly interacting with machines through language, sometimes in contexts where the user may not know they are talking to a machine (like over the phone or a text chatbot). We aim to understand how system designers and researchers might allow their systems to confirm its non-human identity. We collect o... | 2021.acl-long.544 | 10.18653/v1/2021.acl-long.544 | null | 2106.02692 | title_snapshot |
2021.acl-long.545 | Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems | https://aclanthology.org/2021.acl-long.545/ | [
"Claudio Pinhanez",
"Paulo Cavalin",
"Victor Henrique Alves Ribeiro",
"Ana Appel",
"Heloisa Candello",
"Julio Nogima",
"Mauro Pichiliani",
"Melina Guerra",
"Maira de Bayser",
"Gabriel Malfatti",
"Henrique Ferreira"
] | In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies int... | 2021.acl-long.545 | 10.18653/v1/2021.acl-long.545 | null | 2012.09005 | title_judge |
2021.acl-long.546 | Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer | https://aclanthology.org/2021.acl-long.546/ | [
"Fabian Galetzka",
"Jewgeni Rose",
"David Schlangen",
"Jens Lehmann"
] | To improve the coherence and knowledge retrieval capabilities of non-task-oriented dialogue systems, recent Transformer-based models aim to integrate fixed background context. This often comes in the form of knowledge graphs, and the integration is done by creating pseudo utterances through paraphrasing knowledge tripl... | 2021.acl-long.546 | 10.18653/v1/2021.acl-long.546 | null | null | null |
2021.acl-long.547 | DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations | https://aclanthology.org/2021.acl-long.547/ | [
"Dou Hu",
"Lingwei Wei",
"Xiaoyong Huai"
] | Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to e... | 2021.acl-long.547 | 10.18653/v1/2021.acl-long.547 | null | 2106.01978 | title_snapshot |
2021.acl-long.548 | Cross-replication Reliability - An Empirical Approach to Interpreting Inter-rater Reliability | https://aclanthology.org/2021.acl-long.548/ | [
"Ka Wong",
"Praveen Paritosh",
"Lora Aroyo"
] | When collecting annotations and labeled data from humans, a standard practice is to use inter-rater reliability (IRR) as a measure of data goodness (Hallgren, 2012). Metrics such as Krippendorff’s alpha or Cohen’s kappa are typically required to be above a threshold of 0.6 (Landis and Koch, 1977). These absolute thresh... | 2021.acl-long.548 | 10.18653/v1/2021.acl-long.548 | null | 2106.07393 | title_snapshot |
2021.acl-long.549 | TIMEDIAL: Temporal Commonsense Reasoning in Dialog | https://aclanthology.org/2021.acl-long.549/ | [
"Lianhui Qin",
"Aditya Gupta",
"Shyam Upadhyay",
"Luheng He",
"Yejin Choi",
"Manaal Faruqui"
] | Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains largely u... | 2021.acl-long.549 | 10.18653/v1/2021.acl-long.549 | null | 2106.04571 | title_snapshot |
2021.acl-long.550 | RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English) | https://aclanthology.org/2021.acl-long.550/ | [
"Sean Trott",
"Benjamin Bergen"
] | 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 ... | 2021.acl-long.550 | 10.18653/v1/2021.acl-long.550 | null | 2105.13266 | title_snapshot |
2021.acl-long.551 | ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic | https://aclanthology.org/2021.acl-long.551/ | [
"Muhammad Abdul-Mageed",
"AbdelRahim Elmadany",
"El Moatez Billah Nagoudi"
] | Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We re... | 2021.acl-long.551 | 10.18653/v1/2021.acl-long.551 | null | 2101.01785 | title_snapshot |
2021.acl-long.552 | Improving Paraphrase Detection with the Adversarial Paraphrasing Task | https://aclanthology.org/2021.acl-long.552/ | [
"Animesh Nighojkar",
"John Licato"
] | If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instea... | 2021.acl-long.552 | 10.18653/v1/2021.acl-long.552 | null | 2106.07691 | title_snapshot |
2021.acl-long.553 | ADEPT: An Adjective-Dependent Plausibility Task | https://aclanthology.org/2021.acl-long.553/ | [
"Ali Emami",
"Ian Porada",
"Alexandra Olteanu",
"Kaheer Suleman",
"Adam Trischler",
"Jackie Chi Kit Cheung"
] | A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors. While correctly interpreting and assessing the effects of such adjective-noun pairs (e.g., false key) on the plausibility of given events (e.g., opening doors) underpins many natural language unde... | 2021.acl-long.553 | 10.18653/v1/2021.acl-long.553 | null | null | null |
2021.acl-long.554 | ReadOnce Transformers: Reusable Representations of Text for Transformers | https://aclanthology.org/2021.acl-long.554/ | [
"Shih-Ting Lin",
"Ashish Sabharwal",
"Tushar Khot"
] | We present ReadOnce Transformers, an approach to convert a transformer-based model into one that can build an information-capturing, task-independent, and compressed representation of text. The resulting representation is reusable across different examples and tasks, thereby requiring a document shared across many exam... | 2021.acl-long.554 | 10.18653/v1/2021.acl-long.554 | null | 2010.12854 | title_snapshot |
2021.acl-long.555 | Conditional Generation of Temporally-ordered Event Sequences | https://aclanthology.org/2021.acl-long.555/ | [
"Shih-Ting Lin",
"Nathanael Chambers",
"Greg Durrett"
] | Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events. We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new... | 2021.acl-long.555 | 10.18653/v1/2021.acl-long.555 | null | 2012.15786 | title_snapshot |
2021.acl-long.556 | Hate Speech Detection Based on Sentiment Knowledge Sharing | https://aclanthology.org/2021.acl-long.556/ | [
"Xianbing Zhou",
"Yang Yong",
"Xiaochao Fan",
"Ge Ren",
"Yunfeng Song",
"Yufeng Diao",
"Liang Yang",
"Hongfei Lin"
] | The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased traini... | 2021.acl-long.556 | 10.18653/v1/2021.acl-long.556 | null | null | null |
2021.acl-long.557 | Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction | https://aclanthology.org/2021.acl-long.557/ | [
"Tianze Shi",
"Lillian Lee"
] | We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relat... | 2021.acl-long.557 | 10.18653/v1/2021.acl-long.557 | null | 2107.06905 | title_snapshot |
2021.acl-long.558 | SpanNER: Named Entity Re-/Recognition as Span Prediction | https://aclanthology.org/2021.acl-long.558/ | [
"Jinlan Fu",
"Xuanjing Huang",
"Pengfei Liu"
] | Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. Despite its preliminary effectiveness, the span prediction model’s architectural bias has not been fully understood. In this paper, we first investigate the strengths and weaknesses when the sp... | 2021.acl-long.558 | 10.18653/v1/2021.acl-long.558 | null | 2106.00641 | title_snapshot |
2021.acl-long.559 | StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling | https://aclanthology.org/2021.acl-long.559/ | [
"Yikang Shen",
"Yi Tay",
"Che Zheng",
"Dara Bahri",
"Donald Metzler",
"Aaron Courville"
] | There are two major classes of natural language grammars — the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of gramma... | 2021.acl-long.559 | 10.18653/v1/2021.acl-long.559 | null | 2012.00857 | title_snapshot |
2021.acl-long.560 | Language Embeddings for Typology and Cross-lingual Transfer Learning | https://aclanthology.org/2021.acl-long.560/ | [
"Dian Yu",
"Taiqi He",
"Kenji Sagae"
] | Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data. We generate dense ... | 2021.acl-long.560 | 10.18653/v1/2021.acl-long.560 | null | 2106.02082 | title_snapshot |
2021.acl-long.561 | Can Sequence-to-Sequence Models Crack Substitution Ciphers? | https://aclanthology.org/2021.acl-long.561/ | [
"Nada Aldarrab",
"Jonathan May"
] | Decipherment of historical ciphers is a challenging problem. The language of the target plaintext might be unknown, and ciphertext can have a lot of noise. State-of-the-art decipherment methods use beam search and a neural language model to score candidate plaintext hypotheses for a given cipher, assuming the plaintext... | 2021.acl-long.561 | 10.18653/v1/2021.acl-long.561 | null | 2012.15229 | title_snapshot |
2021.acl-long.562 | Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation | https://aclanthology.org/2021.acl-long.562/ | [
"Eleftheria Briakou",
"Marine Carpuat"
] | While it has been shown that Neural Machine Translation (NMT) is highly sensitive to noisy parallel training samples, prior work treats all types of mismatches between source and target as noise. As a result, it remains unclear how samples that are mostly equivalent but contain a small number of semantically divergent ... | 2021.acl-long.562 | 10.18653/v1/2021.acl-long.562 | null | 2105.15087 | title_snapshot |
2021.acl-long.563 | Discriminative Reranking for Neural Machine Translation | https://aclanthology.org/2021.acl-long.563/ | [
"Ann Lee",
"Michael Auli",
"Marc’Aurelio Ranzato"
] | Reranking models enable the integration of rich features to select a better output hypothesis within an n-best list or lattice. These models have a long history in NLP, and we revisit discriminative reranking for modern neural machine translation models by training a large transformer architecture. This takes as input ... | 2021.acl-long.563 | 10.18653/v1/2021.acl-long.563 | null | null | null |
2021.acl-long.564 | Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering | https://aclanthology.org/2021.acl-long.564/ | [
"Siddharth Karamcheti",
"Ranjay Krishna",
"Li Fei-Fei",
"Christopher Manning"
] | Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datas... | 2021.acl-long.564 | 10.18653/v1/2021.acl-long.564 | Outstanding Paper | 2107.02331 | title_snapshot |
2021.acl-long.565 | All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text | https://aclanthology.org/2021.acl-long.565/ | [
"Elizabeth Clark",
"Tal August",
"Sofia Serrano",
"Nikita Haduong",
"Suchin Gururangan",
"Noah A. Smith"
] | Human evaluations are typically considered the gold standard in natural language generation, but as models’ fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts’ ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three do... | 2021.acl-long.565 | 10.18653/v1/2021.acl-long.565 | Outstanding Paper | 2107.00061 | title_snapshot |
2021.acl-long.566 | Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers | https://aclanthology.org/2021.acl-long.566/ | [
"Benjamin Marie",
"Atsushi Fujita",
"Raphael Rubino"
] | This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our study shows that practices for automatic MT evaluation have dramatically changed during the past decade and follow concerning trends. An in... | 2021.acl-long.566 | 10.18653/v1/2021.acl-long.566 | Outstanding Paper | 2106.15195 | title_snapshot |
2021.acl-long.567 | Neural Machine Translation with Monolingual Translation Memory | https://aclanthology.org/2021.acl-long.567/ | [
"Deng Cai",
"Yan Wang",
"Huayang Li",
"Wai Lam",
"Lemao Liu"
] | 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... | 2021.acl-long.567 | 10.18653/v1/2021.acl-long.567 | Outstanding Paper | 2105.11269 | title_snapshot |
2021.acl-long.568 | Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning | https://aclanthology.org/2021.acl-long.568/ | [
"Armen Aghajanyan",
"Sonal Gupta",
"Luke Zettlemoyer"
] | Although pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially in the low data regime. Why can we use relatively vanilla gradient descent algorithms (e.g., without strong re... | 2021.acl-long.568 | 10.18653/v1/2021.acl-long.568 | Outstanding Paper | 2012.13255 | title_snapshot |
2021.acl-long.569 | UnNatural Language Inference | https://aclanthology.org/2021.acl-long.569/ | [
"Koustuv Sinha",
"Prasanna Parthasarathi",
"Joelle Pineau",
"Adina Williams"
] | Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to understand human-like syntax, at least to some extent. We provide novel evidence that complicates this claim: we find that state-of-the-art... | 2021.acl-long.569 | 10.18653/v1/2021.acl-long.569 | Outstanding Paper | 2101.00010 | title_snapshot |
2021.acl-long.570 | Including Signed Languages in Natural Language Processing | https://aclanthology.org/2021.acl-long.570/ | [
"Kayo Yin",
"Amit Moryossef",
"Julie Hochgesang",
"Yoav Goldberg",
"Malihe Alikhani"
] | Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing... | 2021.acl-long.570 | 10.18653/v1/2021.acl-long.570 | Best Theme Paper | 2105.05222 | title_snapshot |
2021.acl-long.571 | Vocabulary Learning via Optimal Transport for Neural Machine Translation | https://aclanthology.org/2021.acl-long.571/ | [
"Jingjing Xu",
"Hao Zhou",
"Chun Gan",
"Zaixiang Zheng",
"Lei Li"
] | The choice of token vocabulary affects the performance of machine translation. This paper aims to figure out what is a good vocabulary and whether we can find the optimal vocabulary without trial training. To answer these questions, we first provide an alternative understanding of vocabulary from the perspective of inf... | 2021.acl-long.571 | 10.18653/v1/2021.acl-long.571 | Best Paper | 2012.15671 | title_snapshot |
2021.acl-short.1 | Catchphrase: Automatic Detection of Cultural References | https://aclanthology.org/2021.acl-short.1/ | [
"Nir Sweed",
"Dafna Shahaf"
] | A snowclone is a customizable phrasal template that can be realized in multiple, instantly recognized variants. For example, “* is the new *" (Orange is the new black, 40 is the new 30). Snowclones are extensively used in social media. In this paper, we study snowclones originating from pop-culture quotes; our goal is ... | 2021.acl-short.1 | 10.18653/v1/2021.acl-short.1 | null | 2106.04830 | title_snapshot |
2021.acl-short.2 | On Training Instance Selection for Few-Shot Neural Text Generation | https://aclanthology.org/2021.acl-short.2/ | [
"Ernie Chang",
"Xiaoyu Shen",
"Hui-Syuan Yeh",
"Vera Demberg"
] | Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little... | 2021.acl-short.2 | 10.18653/v1/2021.acl-short.2 | null | 2107.03176 | title_snapshot |
2021.acl-short.3 | Coreference Resolution without Span Representations | https://aclanthology.org/2021.acl-short.3/ | [
"Yuval Kirstain",
"Ori Ram",
"Omer Levy"
] | The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers. An exception to this trend is coreference resolution, where a sophisticated task-specific model is appended to a pretrained transformer encoder. While highly effective, the model has a very lar... | 2021.acl-short.3 | 10.18653/v1/2021.acl-short.3 | null | 2101.00434 | title_snapshot |
2021.acl-short.4 | Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition | https://aclanthology.org/2021.acl-short.4/ | [
"Chun Chen",
"Fang Kong"
] | In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging. In this paper, we propose a boundary enhanced approach for better Chinese NER. In particular, our approach enhances the boundary information from two perspective... | 2021.acl-short.4 | 10.18653/v1/2021.acl-short.4 | null | null | null |
2021.acl-short.5 | Difficulty-Aware Machine Translation Evaluation | https://aclanthology.org/2021.acl-short.5/ | [
"Runzhe Zhan",
"Xuebo Liu",
"Derek F. Wong",
"Lidia S. Chao"
] | The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and... | 2021.acl-short.5 | 10.18653/v1/2021.acl-short.5 | null | 2107.14402 | title_snapshot |
2021.acl-short.6 | Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition | https://aclanthology.org/2021.acl-short.6/ | [
"Yubo Xie",
"Junze Li",
"Pearl Pu"
] | Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the set-up and the punchline, and further explore the special relation... | 2021.acl-short.6 | 10.18653/v1/2021.acl-short.6 | null | 2012.12007 | title_snapshot |
2021.acl-short.7 | Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer | https://aclanthology.org/2021.acl-short.7/ | [
"Sharmila Reddy Nangi",
"Niyati Chhaya",
"Sopan Khosla",
"Nikhil Kaushik",
"Harshit Nyati"
] | Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to mo... | 2021.acl-short.7 | 10.18653/v1/2021.acl-short.7 | null | null | null |
2021.acl-short.8 | Attention Flows are Shapley Value Explanations | https://aclanthology.org/2021.acl-short.8/ | [
"Kawin Ethayarajh",
"Dan Jurafsky"
] | Shapley Values, a solution to the credit assignment problem in cooperative game theory, are a popular type of explanation in machine learning, having been used to explain the importance of features, embeddings, and even neurons. In NLP, however, leave-one-out and attention-based explanations still predominate. Can we d... | 2021.acl-short.8 | 10.18653/v1/2021.acl-short.8 | null | 2105.14652 | title_snapshot |
2021.acl-short.9 | Video Paragraph Captioning as a Text Summarization Task | https://aclanthology.org/2021.acl-short.9/ | [
"Hui Liu",
"Xiaojun Wan"
] | Video paragraph captioning aims to generate a set of coherent sentences to describe a video that contains several events. Most previous methods simplify this task by using ground-truth event segments. In this work, we propose a novel framework by taking this task as a text summarization task. We first generate lots of ... | 2021.acl-short.9 | 10.18653/v1/2021.acl-short.9 | null | null | null |
2021.acl-short.10 | Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions | https://aclanthology.org/2021.acl-short.10/ | [
"Daniel Rosenberg",
"Itai Gat",
"Amir Feder",
"Roi Reichart"
] | Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to count... | 2021.acl-short.10 | 10.18653/v1/2021.acl-short.10 | null | 2106.04484 | title_snapshot |
2021.acl-short.11 | How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation? | https://aclanthology.org/2021.acl-short.11/ | [
"Sayan Ghosh",
"Zheng Qi",
"Snigdha Chaturvedi",
"Shashank Srivastava"
] | Existing approaches for the Table-to-Text task suffer from issues such as missing information, hallucination and repetition. Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU. In this work, we instead pose the Table-to-Text task as Inverse Re... | 2021.acl-short.11 | 10.18653/v1/2021.acl-short.11 | null | null | null |
2021.acl-short.12 | Automatic Fake News Detection: Are Models Learning to Reason? | https://aclanthology.org/2021.acl-short.12/ | [
"Casper Hansen",
"Christian Hansen",
"Lucas Chaves Lima"
] | Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence. When these models perform well, it is generally assumed to be due to the models havin... | 2021.acl-short.12 | 10.18653/v1/2021.acl-short.12 | null | 2105.07698 | title_snapshot |
2021.acl-short.13 | Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries | https://aclanthology.org/2021.acl-short.13/ | [
"Ashish Shrivastava",
"Kaustubh Dhole",
"Abhinav Bhatt",
"Sharvani Raghunath"
] | Despite end-to-end neural systems making significant progress in the last decade for task-oriented as well as chit-chat based dialogue systems, most dialogue systems rely on hybrid approaches which use a combination of rule-based, retrieval and generative approaches for generating a set of ranked responses. Such dialog... | 2021.acl-short.13 | 10.18653/v1/2021.acl-short.13 | null | 2012.01873 | title_snapshot |
2021.acl-short.14 | N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses | https://aclanthology.org/2021.acl-short.14/ | [
"Karthik Ganesan",
"Pakhi Bamdev",
"Jaivarsan B",
"Amresh Venugopal",
"Abhinav Tushar"
] | 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... | 2021.acl-short.14 | 10.18653/v1/2021.acl-short.14 | null | 2106.06519 | title_snapshot |
2021.acl-short.15 | Gender bias amplification during Speed-Quality optimization in Neural Machine Translation | https://aclanthology.org/2021.acl-short.15/ | [
"Adithya Renduchintala",
"Denise Diaz",
"Kenneth Heafield",
"Xian Li",
"Mona Diab"
] | 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... | 2021.acl-short.15 | 10.18653/v1/2021.acl-short.15 | null | 2106.00169 | title_snapshot |
2021.acl-short.16 | Machine Translation into Low-resource Language Varieties | https://aclanthology.org/2021.acl-short.16/ | [
"Sachin Kumar",
"Antonios Anastasopoulos",
"Shuly Wintner",
"Yulia Tsvetkov"
] | State-of-the-art machine translation (MT) systems are typically trained to generate “standard” target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence... | 2021.acl-short.16 | 10.18653/v1/2021.acl-short.16 | null | 2106.06797 | title_snapshot |
2021.acl-short.17 | Is Sparse Attention more Interpretable? | https://aclanthology.org/2021.acl-short.17/ | [
"Clara Meister",
"Stefan Lazov",
"Isabelle Augenstein",
"Ryan Cotterell"
] | Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs. Yet the attention distribution is typically over representations internal to the model rather than the inputs themselves, suggesting this assumption may not have merit. We build on the recent... | 2021.acl-short.17 | 10.18653/v1/2021.acl-short.17 | null | 2106.01087 | title_snapshot |
2021.acl-short.18 | The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models | https://aclanthology.org/2021.acl-short.18/ | [
"Ulme Wennberg",
"Gustav Eje Henter"
] | Mechanisms for encoding positional information are central for transformer-based language models. In this paper, we analyze the position embeddings of existing language models, finding strong evidence of translation invariance, both for the embeddings themselves and for their effect on self-attention. The degree of tra... | 2021.acl-short.18 | 10.18653/v1/2021.acl-short.18 | null | 2106.01950 | title_snapshot |
2021.acl-short.19 | Relative Importance in Sentence Processing | https://aclanthology.org/2021.acl-short.19/ | [
"Nora Hollenstein",
"Lisa Beinborn"
] | Determining the relative importance of the elements in a sentence is a key factor for effortless natural language understanding. For human language processing, we can approximate patterns of relative importance by measuring reading fixations using eye-tracking technology. In neural language models, gradient-based salie... | 2021.acl-short.19 | 10.18653/v1/2021.acl-short.19 | null | 2106.03471 | title_snapshot |
2021.acl-short.20 | Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models | https://aclanthology.org/2021.acl-short.20/ | [
"Mingyue Han",
"Yinglin Wang"
] | Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this paper, we investigate the problem of semantic similarity bias and reveal the vulnerability of current... | 2021.acl-short.20 | 10.18653/v1/2021.acl-short.20 | null | 2107.01791 | title_snapshot |
2021.acl-short.21 | AND does not mean OR: Using Formal Languages to Study Language Models’ Representations | https://aclanthology.org/2021.acl-short.21/ | [
"Aaron Traylor",
"Roman Feiman",
"Ellie Pavlick"
] | A current open question in natural language processing is to what extent language models, which are trained with access only to the form of language, are able to capture the meaning of language. This question is challenging to answer in general, as there is no clear line between meaning and form, but rather meaning con... | 2021.acl-short.21 | 10.18653/v1/2021.acl-short.21 | null | null | null |
2021.acl-short.22 | Enforcing Consistency in Weakly Supervised Semantic Parsing | https://aclanthology.org/2021.acl-short.22/ | [
"Nitish Gupta",
"Sameer Singh",
"Matt Gardner"
] | The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate search strategies to mitigate the prevalence of spurious programs; however, they typically consider only one input at a time. In this work we exp... | 2021.acl-short.22 | 10.18653/v1/2021.acl-short.22 | null | 2107.05833 | title_snapshot |
2021.acl-short.23 | An Improved Model for Voicing Silent Speech | https://aclanthology.org/2021.acl-short.23/ | [
"David Gaddy",
"Dan Klein"
] | In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals. To give our model greater flexibility to learn its own input features, we directly use EMG signals as input in the place of hand-designed features used by prior work. Our model u... | 2021.acl-short.23 | 10.18653/v1/2021.acl-short.23 | null | 2106.01933 | title_snapshot |
2021.acl-short.24 | What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus | https://aclanthology.org/2021.acl-short.24/ | [
"Alexandra Luccioni",
"Joseph Viviano"
] | Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data. In this exploratory analysis, we delve deeper into the Common Crawl, a colossal web co... | 2021.acl-short.24 | 10.18653/v1/2021.acl-short.24 | null | 2105.02732 | title_judge |
2021.acl-short.25 | Continual Quality Estimation with Online Bayesian Meta-Learning | https://aclanthology.org/2021.acl-short.25/ | [
"Abiola Obamuyide",
"Marina Fomicheva",
"Lucia Specia"
] | Most current quality estimation (QE) models for machine translation are trained and evaluated in a static setting where training and test data are assumed to be from a fixed distribution. However, in real-life settings, the test data that a deployed QE model would be exposed to may differ from its training data. In par... | 2021.acl-short.25 | 10.18653/v1/2021.acl-short.25 | null | null | null |
2021.acl-short.26 | A Span-based Dynamic Local Attention Model for Sequential Sentence Classification | https://aclanthology.org/2021.acl-short.26/ | [
"Xichen Shang",
"Qianli Ma",
"Zhenxi Lin",
"Jiangyue Yan",
"Zipeng Chen"
] | Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous s... | 2021.acl-short.26 | 10.18653/v1/2021.acl-short.26 | null | null | null |
2021.acl-short.27 | How effective is BERT without word ordering? Implications for language understanding and data privacy | https://aclanthology.org/2021.acl-short.27/ | [
"Jack Hessel",
"Alexandra Schofield"
] | Ordered word sequences contain the rich structures that define language. However, it’s often not clear if or how modern pretrained language models utilize these structures. We show that the token representations and self-attention activations within BERT are surprisingly resilient to shuffling the order of input tokens... | 2021.acl-short.27 | 10.18653/v1/2021.acl-short.27 | null | null | null |
2021.acl-short.28 | WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation | https://aclanthology.org/2021.acl-short.28/ | [
"Nachshon Cohen",
"Oren Kalinsky",
"Yftah Ziser",
"Alessandro Moschitti"
] | Recent works made significant advances on summarization tasks, facilitated by summarization datasets. Several existing datasets have the form of coherent-paragraph summaries. However, these datasets were curated from academic documents that were written for experts, thus making the essential step of assessing the summa... | 2021.acl-short.28 | 10.18653/v1/2021.acl-short.28 | null | null | null |
2021.acl-short.29 | UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning | https://aclanthology.org/2021.acl-short.29/ | [
"Hwanhee Lee",
"Seunghyun Yoon",
"Franck Dernoncourt",
"Trung Bui",
"Kyomin Jung"
] | Despite the success of various text generation metrics such as BERTScore, it is still difficult to evaluate the image captions without enough reference captions due to the diversity of the descriptions. In this paper, we introduce a new metric UMIC, an Unreferenced Metric for Image Captioning which does not require ref... | 2021.acl-short.29 | 10.18653/v1/2021.acl-short.29 | null | 2106.14019 | title_snapshot |
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