paper_id stringlengths 17 19 | title stringlengths 27 144 | paper_url stringlengths 43 45 | authors listlengths 1 37 | abstract large_stringlengths 396 1.69k | anthology_id stringlengths 17 19 | doi stringlengths 29 31 | award stringclasses 0
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2021.emnlp-main.601 | Learning with Different Amounts of Annotation: From Zero to Many Labels | https://aclanthology.org/2021.emnlp-main.601/ | [
"Shujian Zhang",
"Chengyue Gong",
"Eunsol Choi"
] | Training NLP systems typically assumes access to annotated data that has a single human label per example. Given imperfect labeling from annotators and inherent ambiguity of language, we hypothesize that single label is not sufficient to learn the spectrum of language interpretation. We explore new annotation distribut... | 2021.emnlp-main.601 | 10.18653/v1/2021.emnlp-main.601 | null | 2109.04408 | title_snapshot |
2021.emnlp-main.602 | When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute | https://aclanthology.org/2021.emnlp-main.602/ | [
"Tao Lei"
] | Large language models have become increasingly difficult to train because of the growing computation time and cost. In this work, we present SRU++, a highly-efficient architecture that combines fast recurrence and attention for sequence modeling. SRU++ exhibits strong modeling capacity and training efficiency. On stand... | 2021.emnlp-main.602 | 10.18653/v1/2021.emnlp-main.602 | null | 2102.12459 | title_snapshot |
2021.emnlp-main.603 | Universal-KD: Attention-based Output-Grounded Intermediate Layer Knowledge Distillation | https://aclanthology.org/2021.emnlp-main.603/ | [
"Yimeng Wu",
"Mehdi Rezagholizadeh",
"Abbas Ghaddar",
"Md Akmal Haidar",
"Ali Ghodsi"
] | Intermediate layer matching is shown as an effective approach for improving knowledge distillation (KD). However, this technique applies matching in the hidden spaces of two different networks (i.e. student and teacher), which lacks clear interpretability. Moreover, intermediate layer KD cannot easily deal with other p... | 2021.emnlp-main.603 | 10.18653/v1/2021.emnlp-main.603 | null | null | null |
2021.emnlp-main.604 | Highly Parallel Autoregressive Entity Linking with Discriminative Correction | https://aclanthology.org/2021.emnlp-main.604/ | [
"Nicola De Cao",
"Wilker Aziz",
"Ivan Titov"
] | Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-paralle... | 2021.emnlp-main.604 | 10.18653/v1/2021.emnlp-main.604 | null | 2109.03792 | title_snapshot |
2021.emnlp-main.605 | Word-Level Coreference Resolution | https://aclanthology.org/2021.emnlp-main.605/ | [
"Vladimir Dobrovolskii"
] | Recent coreference resolution models rely heavily on span representations to find coreference links between word spans. As the number of spans is O(n^2) in the length of text and the number of potential links is O(n^4), various pruning techniques are necessary to make this approach computationally feasible. We propose ... | 2021.emnlp-main.605 | 10.18653/v1/2021.emnlp-main.605 | null | 2109.04127 | title_snapshot |
2021.emnlp-main.606 | A Secure and Efficient Federated Learning Framework for NLP | https://aclanthology.org/2021.emnlp-main.606/ | [
"Chenghong Wang",
"Jieren Deng",
"Xianrui Meng",
"Yijue Wang",
"Ji Li",
"Sheng Lin",
"Shuo Han",
"Fei Miao",
"Sanguthevar Rajasekaran",
"Caiwen Ding"
] | In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks for NLP. Existing solutions under this literature either consider a trusted aggregator or require heavy-weight cryptographic primitives, which makes the performance significantly degraded. Moreover, many existing ... | 2021.emnlp-main.606 | 10.18653/v1/2021.emnlp-main.606 | null | 2201.11934 | title_snapshot |
2021.emnlp-main.607 | Controllable Semantic Parsing via Retrieval Augmentation | https://aclanthology.org/2021.emnlp-main.607/ | [
"Panupong Pasupat",
"Yuan Zhang",
"Kelvin Guu"
] | In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its predictions on certain targeted queries. While we can introduce new training examples exhibiting the target behavior, a mechanism for enacting... | 2021.emnlp-main.607 | 10.18653/v1/2021.emnlp-main.607 | null | 2110.08458 | title_snapshot |
2021.emnlp-main.608 | Constrained Language Models Yield Few-Shot Semantic Parsers | https://aclanthology.org/2021.emnlp-main.608/ | [
"Richard Shin",
"Christopher Lin",
"Sam Thomson",
"Charles Chen",
"Subhro Roy",
"Emmanouil Antonios Platanios",
"Adam Pauls",
"Dan Klein",
"Jason Eisner",
"Benjamin Van Durme"
] | We explore the use of large pretrained language models as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. However, language models are trained to generate natural language. To bridge the gap, we use language models to paraphrase ... | 2021.emnlp-main.608 | 10.18653/v1/2021.emnlp-main.608 | null | 2104.08768 | title_snapshot |
2021.emnlp-main.609 | ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning | https://aclanthology.org/2021.emnlp-main.609/ | [
"Swarnadeep Saha",
"Prateek Yadav",
"Lisa Bauer",
"Mohit Bansal"
] | Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context. Discriminative tasks are limiting because they fail to adequately evaluate the model’s ability to reason and explain predictions with underlying commonsense knowledge. They ... | 2021.emnlp-main.609 | 10.18653/v1/2021.emnlp-main.609 | null | 2104.07644 | title_snapshot |
2021.emnlp-main.610 | Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories | https://aclanthology.org/2021.emnlp-main.610/ | [
"Wenlin Yao",
"Xiaoman Pan",
"Lifeng Jin",
"Jianshu Chen",
"Dian Yu",
"Dong Yu"
] | Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventor... | 2021.emnlp-main.610 | 10.18653/v1/2021.emnlp-main.610 | null | 2110.14091 | title_snapshot |
2021.emnlp-main.611 | LM-Critic: Language Models for Unsupervised Grammatical Error Correction | https://aclanthology.org/2021.emnlp-main.611/ | [
"Michihiro Yasunaga",
"Jure Leskovec",
"Percy Liang"
] | Grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs for training, but obtaining such annotation can be prohibitively expensive. Recently, the Break-It-Fix-It (BIFI) framework has demonstrated strong results on learning to repair a broken program without any labeled ex... | 2021.emnlp-main.611 | 10.18653/v1/2021.emnlp-main.611 | null | 2109.06822 | title_snapshot |
2021.emnlp-main.612 | Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation | https://aclanthology.org/2021.emnlp-main.612/ | [
"Nattapong Tiyajamorn",
"Tomoyuki Kajiwara",
"Yuki Arase",
"Makoto Onizuka"
] | We propose a method to distill a language-agnostic meaning embedding from a multilingual sentence encoder. By removing language-specific information from the original embedding, we retrieve an embedding that fully represents the sentence’s meaning. The proposed method relies only on parallel corpora without any human a... | 2021.emnlp-main.612 | 10.18653/v1/2021.emnlp-main.612 | null | null | null |
2021.emnlp-main.613 | Classifying Dyads for Militarized Conflict Analysis | https://aclanthology.org/2021.emnlp-main.613/ | [
"Niklas Stoehr",
"Lucas Torroba Hennigen",
"Samin Ahbab",
"Robert West",
"Ryan Cotterell"
] | Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships among multiple entities (systemic causes). The aim of this work is t... | 2021.emnlp-main.613 | 10.18653/v1/2021.emnlp-main.613 | null | 2109.12860 | title_snapshot |
2021.emnlp-main.614 | Point-of-Interest Type Prediction using Text and Images | https://aclanthology.org/2021.emnlp-main.614/ | [
"Danae Sánchez Villegas",
"Nikolaos Aletras"
] | Point-of-interest (POI) type prediction is the task of inferring the type of a place from where a social media post was shared. Inferring a POI’s type is useful for studies in computational social science including sociolinguistics, geosemiotics, and cultural geography, and has applications in geosocial networking tech... | 2021.emnlp-main.614 | 10.18653/v1/2021.emnlp-main.614 | null | 2109.00602 | title_snapshot |
2021.emnlp-main.615 | Come hither or go away? Recognising pre-electoral coalition signals in the news | https://aclanthology.org/2021.emnlp-main.615/ | [
"Ines Rehbein",
"Simone Paolo Ponzetto",
"Anna Adendorf",
"Oke Bahnsen",
"Lukas Stoetzer",
"Heiner Stuckenschmidt"
] | In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition. We decompose our problem into two related, but distinct tasks: (i) predictin... | 2021.emnlp-main.615 | 10.18653/v1/2021.emnlp-main.615 | null | null | null |
2021.emnlp-main.616 | #HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention | https://aclanthology.org/2021.emnlp-main.616/ | [
"Yuji Zhang",
"Yubo Zhang",
"Chunpu Xu",
"Jing Li",
"Ziyan Jiang",
"Baolin Peng"
] | Millions of hashtags are created on social media every day to cross-refer messages concerning similar topics. To help people find the topics they want to discuss, this paper characterizes a user’s hashtagging preferences via predicting how likely they will post with a hashtag. It is hypothesized that one’s interests in... | 2021.emnlp-main.616 | 10.18653/v1/2021.emnlp-main.616 | null | null | null |
2021.emnlp-main.617 | Learning Neural Templates for Recommender Dialogue System | https://aclanthology.org/2021.emnlp-main.617/ | [
"Zujie Liang",
"Huang Hu",
"Can Xu",
"Jian Miao",
"Yingying He",
"Yining Chen",
"Xiubo Geng",
"Fan Liang",
"Daxin Jiang"
] | The task of Conversational Recommendation System (CRS), i.e., recommender dialog system, aims to recommend precise items to users through natural language interactions. Though recent end-to-end neural models have shown promising progress on this task, two key challenges still remain. First, the recommended items cannot... | 2021.emnlp-main.617 | 10.18653/v1/2021.emnlp-main.617 | null | 2109.12302 | title_snapshot |
2021.emnlp-main.618 | Proxy Indicators for the Quality of Open-domain Dialogues | https://aclanthology.org/2021.emnlp-main.618/ | [
"Rostislav Nedelchev",
"Jens Lehmann",
"Ricardo Usbeck"
] | The automatic evaluation of open-domain dialogues remains a largely unsolved challenge. Despite the abundance of work done in the field, human judges have to evaluate dialogues’ quality. As a consequence, performing such evaluations at scale is usually expensive. This work investigates using a deep-learning model train... | 2021.emnlp-main.618 | 10.18653/v1/2021.emnlp-main.618 | null | null | null |
2021.emnlp-main.619 | Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering | https://aclanthology.org/2021.emnlp-main.619/ | [
"Or Honovich",
"Leshem Choshen",
"Roee Aharoni",
"Ella Neeman",
"Idan Szpektor",
"Omri Abend"
] | Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability. Inspired by recent work on evaluating factual consistency in abstractive summarization, we propose an automatic evaluati... | 2021.emnlp-main.619 | 10.18653/v1/2021.emnlp-main.619 | null | 2104.08202 | title_snapshot |
2021.emnlp-main.620 | Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking | https://aclanthology.org/2021.emnlp-main.620/ | [
"Weizhe Lin",
"Bo-Hsiang Tseng",
"Bill Byrne"
] | Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances. We present a novel hybrid architecture that augments GPT-2 with representations derived from Graph Attention Networks in such a way to allow causal, sequential prediction of sl... | 2021.emnlp-main.620 | 10.18653/v1/2021.emnlp-main.620 | null | 2104.04466 | title_snapshot |
2021.emnlp-main.621 | A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition | https://aclanthology.org/2021.emnlp-main.621/ | [
"Huimin Wang",
"Kam-Fai Wong"
] | Most reinforcement learning methods for dialog policy learning train a centralized agent that selects a predefined joint action concatenating domain name, intent type, and slot name. The centralized dialog agent suffers from a great many user-agent interaction requirements due to the large action space. Besides, design... | 2021.emnlp-main.621 | 10.18653/v1/2021.emnlp-main.621 | null | null | null |
2021.emnlp-main.622 | Zero-Shot Dialogue State Tracking via Cross-Task Transfer | https://aclanthology.org/2021.emnlp-main.622/ | [
"Zhaojiang Lin",
"Bing Liu",
"Andrea Madotto",
"Seungwhan Moon",
"Zhenpeng Zhou",
"Paul Crook",
"Zhiguang Wang",
"Zhou Yu",
"Eunjoon Cho",
"Rajen Subba",
"Pascale Fung"
] | Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In this work, we propose to transfer the cross-task knowledge from general question answering (QA) corpora for the zero-shot DST task. Specific... | 2021.emnlp-main.622 | 10.18653/v1/2021.emnlp-main.622 | null | 2109.04655 | title_snapshot |
2021.emnlp-main.623 | Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance | https://aclanthology.org/2021.emnlp-main.623/ | [
"Carel van Niekerk",
"Andrey Malinin",
"Christian Geishauser",
"Michael Heck",
"Hsien-chin Lin",
"Nurul Lubis",
"Shutong Feng",
"Milica Gasic"
] | The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking. However, such systems consider only confidence estimates and have difficulty scaling to more complex se... | 2021.emnlp-main.623 | 10.18653/v1/2021.emnlp-main.623 | null | 2109.04349 | title_snapshot |
2021.emnlp-main.624 | Dynamic Forecasting of Conversation Derailment | https://aclanthology.org/2021.emnlp-main.624/ | [
"Yova Kementchedjhieva",
"Anders Søgaard"
] | Online conversations can sometimes take a turn for the worse, either due to systematic cultural differences, accidental misunderstandings, or mere malice. Automatically forecasting derailment in public online conversations provides an opportunity to take early action to moderate it. Previous work in this space is limit... | 2021.emnlp-main.624 | 10.18653/v1/2021.emnlp-main.624 | null | 2110.05111 | title_snapshot |
2021.emnlp-main.625 | A Semantic Filter Based on Relations for Knowledge Graph Completion | https://aclanthology.org/2021.emnlp-main.625/ | [
"Zongwei Liang",
"Junan Yang",
"Hui Liu",
"Keju Huang"
] | Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational capabilities of models in recent years. That is, they investigate better representational models to ... | 2021.emnlp-main.625 | 10.18653/v1/2021.emnlp-main.625 | null | null | null |
2021.emnlp-main.626 | AdapterDrop: On the Efficiency of Adapters in Transformers | https://aclanthology.org/2021.emnlp-main.626/ | [
"Andreas Rücklé",
"Gregor Geigle",
"Max Glockner",
"Tilman Beck",
"Jonas Pfeiffer",
"Nils Reimers",
"Iryna Gurevych"
] | Transformer models are expensive to fine-tune, slow for inference, and have large storage requirements. Recent approaches tackle these shortcomings by training smaller models, dynamically reducing the model size, and by training light-weight adapters. In this paper, we propose AdapterDrop, removing adapters from lower ... | 2021.emnlp-main.626 | 10.18653/v1/2021.emnlp-main.626 | null | 2010.11918 | title_snapshot |
2021.emnlp-main.627 | Understanding and Overcoming the Challenges of Efficient Transformer Quantization | https://aclanthology.org/2021.emnlp-main.627/ | [
"Yelysei Bondarenko",
"Markus Nagel",
"Tijmen Blankevoort"
] | Transformer-based architectures have become the de-facto standard models for a wide range of Natural Language Processing tasks. However, their memory footprint and high latency are prohibitive for efficient deployment and inference on resource-limited devices. In this work, we explore quantization for transformers. We ... | 2021.emnlp-main.627 | 10.18653/v1/2021.emnlp-main.627 | null | 2109.12948 | title_snapshot |
2021.emnlp-main.628 | CAPE: Context-Aware Private Embeddings for Private Language Learning | https://aclanthology.org/2021.emnlp-main.628/ | [
"Richard Plant",
"Dimitra Gkatzia",
"Valerio Giuffrida"
] | Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others. However, obtaining text representations or embeddings using these models risks encoding personally identifiable information learned from language and... | 2021.emnlp-main.628 | 10.18653/v1/2021.emnlp-main.628 | null | 2108.12318 | title_snapshot |
2021.emnlp-main.629 | Text Detoxification using Large Pre-trained Neural Models | https://aclanthology.org/2021.emnlp-main.629/ | [
"David Dale",
"Anton Voronov",
"Daryna Dementieva",
"Varvara Logacheva",
"Olga Kozlova",
"Nikita Semenov",
"Alexander Panchenko"
] | We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform style transfer. We use a well-performing paraphraser guided by style-t... | 2021.emnlp-main.629 | 10.18653/v1/2021.emnlp-main.629 | null | 2109.08914 | title_snapshot |
2021.emnlp-main.630 | Document-Level Text Simplification: Dataset, Criteria and Baseline | https://aclanthology.org/2021.emnlp-main.630/ | [
"Renliang Sun",
"Hanqi Jin",
"Xiaojun Wan"
] | Text simplification is a valuable technique. However, current research is limited to sentence simplification. In this paper, we define and investigate a new task of document-level text simplification, which aims to simplify a document consisting of multiple sentences. Based on Wikipedia dumps, we first construct a larg... | 2021.emnlp-main.630 | 10.18653/v1/2021.emnlp-main.630 | null | 2110.05071 | title_snapshot |
2021.emnlp-main.631 | A Bag of Tricks for Dialogue Summarization | https://aclanthology.org/2021.emnlp-main.631/ | [
"Muhammad Khalifa",
"Miguel Ballesteros",
"Kathleen McKeown"
] | Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation,... | 2021.emnlp-main.631 | 10.18653/v1/2021.emnlp-main.631 | null | 2109.08232 | title_snapshot |
2021.emnlp-main.632 | Paraphrasing Compound Nominalizations | https://aclanthology.org/2021.emnlp-main.632/ | [
"John Lee",
"Ho Hung Lim",
"Carol Webster"
] | A nominalization uses a deverbal noun to describe an event associated with its underlying verb. Commonly found in academic and formal texts, nominalizations can be difficult to interpret because of ambiguous semantic relations between the deverbal noun and its arguments. Our goal is to interpret nominalizations by gene... | 2021.emnlp-main.632 | 10.18653/v1/2021.emnlp-main.632 | null | null | null |
2021.emnlp-main.633 | Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation | https://aclanthology.org/2021.emnlp-main.633/ | [
"Clement Rebuffel",
"Thomas Scialom",
"Laure Soulier",
"Benjamin Piwowarski",
"Sylvain Lamprier",
"Jacopo Staiano",
"Geoffrey Scoutheeten",
"Patrick Gallinari"
] | QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions. Its adaptation to Data-to-Text tasks is not straightforward, as it requires multimodal Question Generation and Answering systems on the con... | 2021.emnlp-main.633 | 10.18653/v1/2021.emnlp-main.633 | null | 2104.07555 | title_snapshot |
2021.emnlp-main.634 | Low-Rank Subspaces for Unsupervised Entity Linking | https://aclanthology.org/2021.emnlp-main.634/ | [
"Akhil Arora",
"Alberto Garcia-Duran",
"Robert West"
] | Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity linking method, Eigenthemes, that relies solely on the availabi... | 2021.emnlp-main.634 | 10.18653/v1/2021.emnlp-main.634 | null | 2104.08737 | title_snapshot |
2021.emnlp-main.635 | TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations | https://aclanthology.org/2021.emnlp-main.635/ | [
"Xianming Li",
"Xiaotian Luo",
"Chenghao Dong",
"Daichuan Yang",
"Beidi Luan",
"Zhen He"
] | Joint extraction of entities and relations from unstructured texts to form factual triples is a fundamental task of constructing a Knowledge Base (KB). A common method is to decode triples by predicting entity pairs to obtain the corresponding relation. However, it is still challenging to handle this task efficiently, ... | 2021.emnlp-main.635 | 10.18653/v1/2021.emnlp-main.635 | null | null | null |
2021.emnlp-main.636 | Extracting Event Temporal Relations via Hyperbolic Geometry | https://aclanthology.org/2021.emnlp-main.636/ | [
"Xingwei Tan",
"Gabriele Pergola",
"Yulan He"
] | Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in ... | 2021.emnlp-main.636 | 10.18653/v1/2021.emnlp-main.636 | null | 2109.05527 | title_snapshot |
2021.emnlp-main.637 | Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention | https://aclanthology.org/2021.emnlp-main.637/ | [
"Jiawei Chen",
"Hongyu Lin",
"Xianpei Han",
"Le Sun"
] | Event detection has long been troubled by the trigger curse: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection performance. This problem is even more severe in few-shot scenario. In this paper, we identify and solve the trigger curse problem in few-shot event det... | 2021.emnlp-main.637 | 10.18653/v1/2021.emnlp-main.637 | null | 2109.05747 | title_snapshot |
2021.emnlp-main.638 | Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction | https://aclanthology.org/2021.emnlp-main.638/ | [
"Asahi Ushio",
"Federico Liberatore",
"Jose Camacho-Collados"
] | Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. In particular, term weighting is the basis for keyword extraction. However, there are relatively few evaluation studies that shed light about the strengths and shortcomings of each weighting scheme. In fact, in most cases r... | 2021.emnlp-main.638 | 10.18653/v1/2021.emnlp-main.638 | null | 2104.08028 | title_snapshot |
2021.emnlp-main.639 | Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework | https://aclanthology.org/2021.emnlp-main.639/ | [
"Zhen Han",
"Gengyuan Zhang",
"Yunpu Ma",
"Volker Tresp"
] | Various temporal knowledge graph (KG) completion models have been proposed in the recent literature. The models usually contain two parts, a temporal embedding layer and a score function derived from existing static KG modeling approaches. Since the approaches differ along several dimensions, including different score ... | 2021.emnlp-main.639 | 10.18653/v1/2021.emnlp-main.639 | null | null | null |
2021.emnlp-main.640 | Matching-oriented Embedding Quantization For Ad-hoc Retrieval | https://aclanthology.org/2021.emnlp-main.640/ | [
"Shitao Xiao",
"Zheng Liu",
"Yingxia Shao",
"Defu Lian",
"Xing Xie"
] | Product quantization (PQ) is a widely used technique for ad-hoc retrieval. Recent studies propose supervised PQ, where the embedding and quantization models can be jointly trained with supervised learning. However, there is a lack of appropriate formulation of the joint training objective; thus, the improvements over p... | 2021.emnlp-main.640 | 10.18653/v1/2021.emnlp-main.640 | null | 2104.07858 | title_judge |
2021.emnlp-main.641 | Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement | https://aclanthology.org/2021.emnlp-main.641/ | [
"Mengting Hu",
"Honglei Guo",
"Shiwan Zhao",
"Hang Gao",
"Zhong Su"
] | A mind-map is a diagram that represents the central concept and key ideas in a hierarchical way. Converting plain text into a mind-map will reveal its key semantic structure and be easier to understand. Given a document, the existing automatic mind-map generation method extracts the relationships of every sentence pair... | 2021.emnlp-main.641 | 10.18653/v1/2021.emnlp-main.641 | null | 2109.02457 | title_snapshot |
2021.emnlp-main.642 | Deep Attention Diffusion Graph Neural Networks for Text Classification | https://aclanthology.org/2021.emnlp-main.642/ | [
"Yonghao Liu",
"Renchu Guan",
"Fausto Giunchiglia",
"Yanchun Liang",
"Xiaoyue Feng"
] | Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods an... | 2021.emnlp-main.642 | 10.18653/v1/2021.emnlp-main.642 | null | null | null |
2021.emnlp-main.643 | Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution | https://aclanthology.org/2021.emnlp-main.643/ | [
"Yi Huang",
"Buse Giledereli",
"Abdullatif Köksal",
"Arzucan Özgür",
"Elif Ozkirimli"
] | Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Resampling and re-weighting are common approaches used for addressing the class imbalance problem, however, they are not effective when there i... | 2021.emnlp-main.643 | 10.18653/v1/2021.emnlp-main.643 | null | 2109.04712 | title_snapshot |
2021.emnlp-main.644 | Bayesian Topic Regression for Causal Inference | https://aclanthology.org/2021.emnlp-main.644/ | [
"Maximilian Ahrens",
"Julian Ashwin",
"Jan-Peter Calliess",
"Vu Nguyen"
] | Causal inference using observational text data is becoming increasingly popular in many research areas. This paper presents the Bayesian Topic Regression (BTR) model that uses both text and numerical information to model an outcome variable. It allows estimation of both discrete and continuous treatment effects. Furthe... | 2021.emnlp-main.644 | 10.18653/v1/2021.emnlp-main.644 | null | 2109.05317 | title_snapshot |
2021.emnlp-main.645 | Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience | https://aclanthology.org/2021.emnlp-main.645/ | [
"George Chrysostomou",
"Nikolaos Aletras"
] | Pretrained transformer-based models such as BERT have demonstrated state-of-the-art predictive performance when adapted into a range of natural language processing tasks. An open problem is how to improve the faithfulness of explanations (rationales) for the predictions of these models. In this paper, we hypothesize th... | 2021.emnlp-main.645 | 10.18653/v1/2021.emnlp-main.645 | null | 2108.13759 | title_snapshot |
2021.emnlp-main.646 | What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models | https://aclanthology.org/2021.emnlp-main.646/ | [
"Mor Geva",
"Uri Katz",
"Aviv Ben-Arie",
"Jonathan Berant"
] | The primary paradigm for multi-task training in natural language processing is to represent the input with a shared pre-trained language model, and add a small, thin network (head) per task. Given an input, a target head is the head that is selected for outputting the final prediction. In this work, we examine the beha... | 2021.emnlp-main.646 | 10.18653/v1/2021.emnlp-main.646 | null | 2104.06129 | title_snapshot |
2021.emnlp-main.647 | Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks | https://aclanthology.org/2021.emnlp-main.647/ | [
"Nathaniel Berger",
"Stefan Riezler",
"Sebastian Ebert",
"Artem Sokolov"
] | Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP). A central research topic has been the investigation of search algorithms and search constraints, accompanied by benchmark algorithms and tasks. We implement an algorithm inspired by zeroth order optim... | 2021.emnlp-main.647 | 10.18653/v1/2021.emnlp-main.647 | null | 2109.07926 | title_snapshot |
2021.emnlp-main.648 | Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods | https://aclanthology.org/2021.emnlp-main.648/ | [
"Peru Bhardwaj",
"John Kelleher",
"Luca Costabello",
"Declan O’Sullivan"
] | Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poisoning attacks against KGE models for link prediction. These attacks craft adversarial additions or deletions at training time to cause model f... | 2021.emnlp-main.648 | 10.18653/v1/2021.emnlp-main.648 | null | 2111.03120 | title_snapshot |
2021.emnlp-main.649 | Locke’s Holiday: Belief Bias in Machine Reading | https://aclanthology.org/2021.emnlp-main.649/ | [
"Anders Søgaard"
] | I highlight a simple failure mode of state-of-the-art machine reading systems: when contexts do not align with commonly shared beliefs. For example, machine reading systems fail to answer What did Elizabeth want? correctly in the context of ‘My kingdom for a cough drop, cried Queen Elizabeth.’ Biased by co-occurrence s... | 2021.emnlp-main.649 | 10.18653/v1/2021.emnlp-main.649 | null | null | null |
2021.emnlp-main.650 | Sequence Length is a Domain: Length-based Overfitting in Transformer Models | https://aclanthology.org/2021.emnlp-main.650/ | [
"Dusan Varis",
"Ondřej Bojar"
] | Transformer-based sequence-to-sequence architectures, while achieving state-of-the-art results on a large number of NLP tasks, can still suffer from overfitting during training. In practice, this is usually countered either by applying regularization methods (e.g. dropout, L2-regularization) or by providing huge amount... | 2021.emnlp-main.650 | 10.18653/v1/2021.emnlp-main.650 | null | 2109.07276 | title_snapshot |
2021.emnlp-main.651 | Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification | https://aclanthology.org/2021.emnlp-main.651/ | [
"Maximilian Mozes",
"Max Bartolo",
"Pontus Stenetorp",
"Bennett Kleinberg",
"Lewis Griffin"
] | Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality). Enforcing constraints to uph... | 2021.emnlp-main.651 | 10.18653/v1/2021.emnlp-main.651 | null | 2109.04385 | title_snapshot |
2021.emnlp-main.652 | Is Information Density Uniform in Task-Oriented Dialogues? | https://aclanthology.org/2021.emnlp-main.652/ | [
"Mario Giulianelli",
"Arabella Sinclair",
"Raquel Fernández"
] | The Uniform Information Density principle states that speakers plan their utterances to reduce fluctuations in the density of the information transmitted. In this paper, we test whether, and within which contextual units this principle holds in task-oriented dialogues. We show that there is evidence supporting the prin... | 2021.emnlp-main.652 | 10.18653/v1/2021.emnlp-main.652 | null | null | null |
2021.emnlp-main.653 | On Homophony and Rényi Entropy | https://aclanthology.org/2021.emnlp-main.653/ | [
"Tiago Pimentel",
"Clara Meister",
"Simone Teufel",
"Ryan Cotterell"
] | Homophony’s widespread presence in natural languages is a controversial topic. Recent theories of language optimality have tried to justify its prevalence, despite its negative effects on cognitive processing time, e.g., Piantadosi et al. (2012) argued homophony enables the reuse of efficient wordforms and is thus bene... | 2021.emnlp-main.653 | 10.18653/v1/2021.emnlp-main.653 | null | 2109.13766 | title_snapshot |
2021.emnlp-main.654 | Synthetic Textual Features for the Large-Scale Detection of Basic-level Categories in English and Mandarin | https://aclanthology.org/2021.emnlp-main.654/ | [
"Yiwen Chen",
"Simone Teufel"
] | Basic-level categories (BLC) are an important psycholinguistic concept introduced by Rosch et al. (1976); they are defined as the most inclusive categories for which a concrete mental image of the category as a whole can be formed, and also as those categories which are acquired early in life. Rosch’s original algorith... | 2021.emnlp-main.654 | 10.18653/v1/2021.emnlp-main.654 | null | null | null |
2021.emnlp-main.655 | TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting | https://aclanthology.org/2021.emnlp-main.655/ | [
"Haohai Sun",
"Jialun Zhong",
"Yunpu Ma",
"Zhen Han",
"Kun He"
] | Temporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing research interest in recent years. Most existing methods focus on reasoning at past timestamps to complete the missing facts, and there are only a few works of reasoning on known TKGs to forecast future facts. Compared with the comple... | 2021.emnlp-main.655 | 10.18653/v1/2021.emnlp-main.655 | null | 2109.04101 | title_snapshot |
2021.emnlp-main.656 | Code-switched inspired losses for spoken dialog representations | https://aclanthology.org/2021.emnlp-main.656/ | [
"Pierre Colombo",
"Emile Chapuis",
"Matthieu Labeau",
"Chloé Clavel"
] | Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (e.g in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model t... | 2021.emnlp-main.656 | 10.18653/v1/2021.emnlp-main.656 | null | 2108.12465 | title_judge |
2021.emnlp-main.657 | BiQUE: Biquaternionic Embeddings of Knowledge Graphs | https://aclanthology.org/2021.emnlp-main.657/ | [
"Jia Guo",
"Stanley Kok"
] | Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs). Existing KGE models rely on geometric operations to model relational patterns. Euclidean (circular) rotation is useful for modeling patterns such as symmetry, but cannot represent hierarchical semantics. In contrast, hyperbolic ... | 2021.emnlp-main.657 | 10.18653/v1/2021.emnlp-main.657 | null | 2109.14401 | title_snapshot |
2021.emnlp-main.658 | Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs | https://aclanthology.org/2021.emnlp-main.658/ | [
"Zhen Han",
"Zifeng Ding",
"Yunpu Ma",
"Yujia Gu",
"Volker Tresp"
] | There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary di... | 2021.emnlp-main.658 | 10.18653/v1/2021.emnlp-main.658 | null | null | null |
2021.emnlp-main.659 | RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models | https://aclanthology.org/2021.emnlp-main.659/ | [
"Wenkai Yang",
"Yankai Lin",
"Peng Li",
"Jie Zhou",
"Xu Sun"
] | Backdoor attacks, which maliciously control a well-trained model’s outputs of the instances with specific triggers, are recently shown to be serious threats to the safety of reusing deep neural networks (DNNs). In this work, we propose an efficient online defense mechanism based on robustness-aware perturbations. Speci... | 2021.emnlp-main.659 | 10.18653/v1/2021.emnlp-main.659 | null | 2110.07831 | title_snapshot |
2021.emnlp-main.660 | FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations | https://aclanthology.org/2021.emnlp-main.660/ | [
"Lukas Lange",
"Heike Adel",
"Jannik Strötgen",
"Dietrich Klakow"
] | Combining several embeddings typically improves performance in downstream tasks as different embeddings encode different information. It has been shown that even models using embeddings from transformers still benefit from the inclusion of standard word embeddings. However, the combination of embeddings of different ty... | 2021.emnlp-main.660 | 10.18653/v1/2021.emnlp-main.660 | null | 2010.12305 | title_snapshot |
2021.emnlp-main.661 | A Strong Baseline for Query Efficient Attacks in a Black Box Setting | https://aclanthology.org/2021.emnlp-main.661/ | [
"Rishabh Maheshwary",
"Saket Maheshwary",
"Vikram Pudi"
] | Existing black box search methods have achieved high success rate in generating adversarial attacks against NLP models. However, such search methods are inefficient as they do not consider the amount of queries required to generate adversarial attacks. Also, prior attacks do not maintain a consistent search space while... | 2021.emnlp-main.661 | 10.18653/v1/2021.emnlp-main.661 | null | 2109.04775 | title_snapshot |
2021.emnlp-main.662 | Machine Translation Decoding beyond Beam Search | https://aclanthology.org/2021.emnlp-main.662/ | [
"Rémi Leblond",
"Jean-Baptiste Alayrac",
"Laurent Sifre",
"Miruna Pislar",
"Lespiau Jean-Baptiste",
"Ioannis Antonoglou",
"Karen Simonyan",
"Oriol Vinyals"
] | Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish ... | 2021.emnlp-main.662 | 10.18653/v1/2021.emnlp-main.662 | null | 2104.05336 | title_snapshot |
2021.emnlp-main.663 | Document Graph for Neural Machine Translation | https://aclanthology.org/2021.emnlp-main.663/ | [
"Mingzhou Xu",
"Liangyou Li",
"Derek F. Wong",
"Qun Liu",
"Lidia S. Chao"
] | Previous works have shown that contextual information can improve the performance of neural machine translation (NMT). However, most existing document-level NMT methods failed to leverage contexts beyond a few set of previous sentences. How to make use of the whole document as global contexts is still a challenge. To a... | 2021.emnlp-main.663 | 10.18653/v1/2021.emnlp-main.663 | null | 2012.03477 | title_snapshot |
2021.emnlp-main.664 | An Empirical Investigation of Word Alignment Supervision for Zero-Shot Multilingual Neural Machine Translation | https://aclanthology.org/2021.emnlp-main.664/ | [
"Alessandro Raganato",
"Raúl Vázquez",
"Mathias Creutz",
"Jörg Tiedemann"
] | Zero-shot translations is a fascinating feature of Multilingual Neural Machine Translation (MNMT) systems. These MNMT models are usually trained on English-centric data, i.e. English either as the source or target language, and with a language label prepended to the input indicating the target language. However, recent... | 2021.emnlp-main.664 | 10.18653/v1/2021.emnlp-main.664 | null | null | null |
2021.emnlp-main.665 | Graph Algorithms for Multiparallel Word Alignment | https://aclanthology.org/2021.emnlp-main.665/ | [
"Ayyoob Imani",
"Masoud Jalili Sabet",
"Lutfi Kerem Senel",
"Philipp Dufter",
"François Yvon",
"Hinrich Schütze"
] | With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently. Alignments are useful for typological research, transferring formatting like markup to translated texts, and can be used in t... | 2021.emnlp-main.665 | 10.18653/v1/2021.emnlp-main.665 | null | 2109.06283 | title_snapshot |
2021.emnlp-main.666 | Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation | https://aclanthology.org/2021.emnlp-main.666/ | [
"Eva Hasler",
"Tobias Domhan",
"Jonay Trenous",
"Ke Tran",
"Bill Byrne",
"Felix Hieber"
] | Building neural machine translation systems to perform well on a specific target domain is a well-studied problem. Optimizing system performance for multiple, diverse target domains however remains a challenge. We study this problem in an adaptation setting where the goal is to preserve the existing system quality whil... | 2021.emnlp-main.666 | 10.18653/v1/2021.emnlp-main.666 | null | null | null |
2021.emnlp-main.667 | Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT | https://aclanthology.org/2021.emnlp-main.667/ | [
"Elena Voita",
"Rico Sennrich",
"Ivan Titov"
] | Differently from the traditional statistical MT that decomposes the translation task into distinct separately learned components, neural machine translation uses a single neural network to model the entire translation process. Despite neural machine translation being de-facto standard, it is still not clear how NMT mod... | 2021.emnlp-main.667 | 10.18653/v1/2021.emnlp-main.667 | null | 2109.01396 | title_snapshot |
2021.emnlp-main.668 | Effective Fine-Tuning Methods for Cross-lingual Adaptation | https://aclanthology.org/2021.emnlp-main.668/ | [
"Tao Yu",
"Shafiq Joty"
] | Large scale multilingual pre-trained language models have shown promising results in zero- and few-shot cross-lingual tasks. However, recent studies have shown their lack of generalizability when the languages are structurally dissimilar. In this work, we propose a novel fine-tuning method based on co-training that aim... | 2021.emnlp-main.668 | 10.18653/v1/2021.emnlp-main.668 | null | null | null |
2021.emnlp-main.669 | Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach | https://aclanthology.org/2021.emnlp-main.669/ | [
"Víctor M. Sánchez-Cartagena",
"Miquel Esplà-Gomis",
"Juan Antonio Pérez-Ortiz",
"Felipe Sánchez-Martínez"
] | In the context of neural machine translation, data augmentation (DA) techniques may be used for generating additional training samples when the available parallel data are scarce. Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent ... | 2021.emnlp-main.669 | 10.18653/v1/2021.emnlp-main.669 | null | 2109.03645 | title_snapshot |
2021.emnlp-main.670 | Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution | https://aclanthology.org/2021.emnlp-main.670/ | [
"Denis Emelin",
"Rico Sennrich"
] | Winograd schemas are a well-established tool for evaluating coreference resolution (CoR) and commonsense reasoning (CSR) capabilities of computational models. So far, schemas remained largely confined to English, limiting their utility in multilingual settings. This work presents Wino-X, a parallel dataset of German, F... | 2021.emnlp-main.670 | 10.18653/v1/2021.emnlp-main.670 | null | null | null |
2021.emnlp-main.671 | One Source, Two Targets: Challenges and Rewards of Dual Decoding | https://aclanthology.org/2021.emnlp-main.671/ | [
"Jitao Xu",
"François Yvon"
] | Machine translation is generally understood as generating one target text from an input source document. In this paper, we consider a stronger requirement: to jointly generate two texts so that each output side effectively depends on the other. As we discuss, such a device serves several practical purposes, from multi-... | 2021.emnlp-main.671 | 10.18653/v1/2021.emnlp-main.671 | null | 2109.10197 | title_snapshot |
2021.emnlp-main.672 | Discrete and Soft Prompting for Multilingual Models | https://aclanthology.org/2021.emnlp-main.672/ | [
"Mengjie Zhao",
"Hinrich Schütze"
] | It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs). In this paper, we show that discrete and soft prompting perform better than finetuning in multilingual cases: Crosslingual transfer and in-language training of multilingual natura... | 2021.emnlp-main.672 | 10.18653/v1/2021.emnlp-main.672 | null | 2109.03630 | title_snapshot |
2021.emnlp-main.673 | Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models | https://aclanthology.org/2021.emnlp-main.673/ | [
"Jiaoda Li",
"Duygu Ataman",
"Rico Sennrich"
] | Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an ... | 2021.emnlp-main.673 | 10.18653/v1/2021.emnlp-main.673 | null | 2109.03415 | title_snapshot |
2021.emnlp-main.674 | Efficient Inference for Multilingual Neural Machine Translation | https://aclanthology.org/2021.emnlp-main.674/ | [
"Alexandre Berard",
"Dain Lee",
"Stephane Clinchant",
"Kweonwoo Jung",
"Vassilina Nikoulina"
] | Multilingual NMT has become an attractive solution for MT deployment in production. But to match bilingual quality, it comes at the cost of larger and slower models. In this work, we consider several ways to make multilingual NMT faster at inference without degrading its quality. We experiment with several “light decod... | 2021.emnlp-main.674 | 10.18653/v1/2021.emnlp-main.674 | null | 2109.06679 | title_snapshot |
2021.emnlp-main.675 | Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages | https://aclanthology.org/2021.emnlp-main.675/ | [
"Tejas Dhamecha",
"Rudra Murthy",
"Samarth Bharadwaj",
"Karthik Sankaranarayanan",
"Pushpak Bhattacharyya"
] | We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tune... | 2021.emnlp-main.675 | 10.18653/v1/2021.emnlp-main.675 | null | 2109.10534 | title_snapshot |
2021.emnlp-main.676 | Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification | https://aclanthology.org/2021.emnlp-main.676/ | [
"Daria Pylypenko",
"Kwabena Amponsah-Kaakyire",
"Koel Dutta Chowdhury",
"Josef van Genabith",
"Cristina España-Bonet"
] | Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts. By contrast, to date, neural architectures without manual feature engineering have been less explored for this task. In this work, we (i) compare the traditional featur... | 2021.emnlp-main.676 | 10.18653/v1/2021.emnlp-main.676 | null | 2109.07604 | title_snapshot |
2021.emnlp-main.677 | Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search Degradation | https://aclanthology.org/2021.emnlp-main.677/ | [
"Ivan Provilkov",
"Andrey Malinin"
] | Neural Machine Translation (NMT) is known to suffer from a beam-search problem: after a certain point, increasing beam size causes an overall drop in translation quality. This effect is especially pronounced for long sentences. While much work was done analyzing this phenomenon, primarily for autoregressive NMT models,... | 2021.emnlp-main.677 | 10.18653/v1/2021.emnlp-main.677 | null | 2109.06253 | title_snapshot |
2021.emnlp-main.678 | Cross-Policy Compliance Detection via Question Answering | https://aclanthology.org/2021.emnlp-main.678/ | [
"Marzieh Saeidi",
"Majid Yazdani",
"Andreas Vlachos"
] | Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e.g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines). This task has been previously instantiated as a form of textual entailment, which results in poor accuracy due ... | 2021.emnlp-main.678 | 10.18653/v1/2021.emnlp-main.678 | null | 2109.03731 | title_snapshot |
2021.emnlp-main.679 | Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification | https://aclanthology.org/2021.emnlp-main.679/ | [
"Ran Wang",
"Xi’ao Su",
"Siyu Long",
"Xinyu Dai",
"Shujian Huang",
"Jiajun Chen"
] | Large-scale multi-label text classification (LMTC) tasks often face long-tailed label distributions, where many labels have few or even no training instances. Although current methods can exploit prior knowledge to handle these few/zero-shot labels, they neglect the meta-knowledge contained in the dataset that can guid... | 2021.emnlp-main.679 | 10.18653/v1/2021.emnlp-main.679 | null | null | null |
2021.emnlp-main.680 | Unsupervised Multi-View Post-OCR Error Correction With Language Models | https://aclanthology.org/2021.emnlp-main.680/ | [
"Harsh Gupta",
"Luciano Del Corro",
"Samuel Broscheit",
"Johannes Hoffart",
"Eliot Brenner"
] | We investigate post-OCR correction in a setting where we have access to different OCR views of the same document. The goal of this study is to understand if a pretrained language model (LM) can be used in an unsupervised way to reconcile the different OCR views such that their combination contains fewer errors than eac... | 2021.emnlp-main.680 | 10.18653/v1/2021.emnlp-main.680 | null | null | null |
2021.emnlp-main.681 | Parallel Refinements for Lexically Constrained Text Generation with BART | https://aclanthology.org/2021.emnlp-main.681/ | [
"Xingwei He"
] | Lexically constrained text generation aims to control the generated text by incorporating certain pre-specified keywords into the output. Previous work injects lexical constraints into the output by controlling the decoding process or refining the candidate output iteratively, which tends to generate generic or ungramm... | 2021.emnlp-main.681 | 10.18653/v1/2021.emnlp-main.681 | null | 2109.12487 | title_snapshot |
2021.emnlp-main.682 | BERT-Beta: A Proactive Probabilistic Approach to Text Moderation | https://aclanthology.org/2021.emnlp-main.682/ | [
"Fei Tan",
"Yifan Hu",
"Kevin Yen",
"Changwei Hu"
] | Text moderation for user generated content, which helps to promote healthy interaction among users, has been widely studied and many machine learning models have been proposed. In this work, we explore an alternative perspective by augmenting reactive reviews with proactive forecasting. Specifically, we propose a new c... | 2021.emnlp-main.682 | 10.18653/v1/2021.emnlp-main.682 | null | 2109.08805 | title_snapshot |
2021.emnlp-main.683 | STaCK: Sentence Ordering with Temporal Commonsense Knowledge | https://aclanthology.org/2021.emnlp-main.683/ | [
"Deepanway Ghosal",
"Navonil Majumder",
"Rada Mihalcea",
"Soujanya Poria"
] | Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological sequence of events described in the text. Document-level contextual understanding and commonsense know... | 2021.emnlp-main.683 | 10.18653/v1/2021.emnlp-main.683 | null | 2109.02247 | title_snapshot |
2021.emnlp-main.684 | Preventing Author Profiling through Zero-Shot Multilingual Back-Translation | https://aclanthology.org/2021.emnlp-main.684/ | [
"David Ifeoluwa Adelani",
"Miaoran Zhang",
"Xiaoyu Shen",
"Ali Davody",
"Thomas Kleinbauer",
"Dietrich Klakow"
] | Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g. their gender or ethnicity. Style transfer is an effective way of transforming texts in order to remove any information that enables author profiling. However, for a number of current state-of-the-a... | 2021.emnlp-main.684 | 10.18653/v1/2021.emnlp-main.684 | null | 2109.09133 | title_snapshot |
2021.emnlp-main.685 | CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation | https://aclanthology.org/2021.emnlp-main.685/ | [
"Yue Wang",
"Weishi Wang",
"Shafiq Joty",
"Steven C.H. Hoi"
] | Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently shown to transfer well to Programming Languages (PL) and largely benefit a broad set of code-related tasks. Despite their success, most current methods either rely on an encoder-only (or decoder-only) pre-training that is suboptimal for ... | 2021.emnlp-main.685 | 10.18653/v1/2021.emnlp-main.685 | null | 2109.00859 | title_snapshot |
2021.emnlp-main.686 | Detect and Classify – Joint Span Detection and Classification for Health Outcomes | https://aclanthology.org/2021.emnlp-main.686/ | [
"Micheal Abaho",
"Danushka Bollegala",
"Paula Williamson",
"Susanna Dodd"
] | A health outcome is a measurement or an observation used to capture and assess the effect of a treatment. Automatic detection of health outcomes from text would undoubtedly speed up access to evidence necessary in healthcare decision making. Prior work on outcome detection has modelled this task as either (a) a sequenc... | 2021.emnlp-main.686 | 10.18653/v1/2021.emnlp-main.686 | null | 2104.07789 | title_snapshot |
2021.emnlp-main.687 | Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems | https://aclanthology.org/2021.emnlp-main.687/ | [
"Zheng Yuan",
"Shiva Taslimipoor",
"Christopher Davis",
"Christopher Bryant"
] | In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English. Specifically, we first develop a new state-of-the-art binary detection system based on pre-trained ELECTRA, and then extend it to multi-class detection using different... | 2021.emnlp-main.687 | 10.18653/v1/2021.emnlp-main.687 | null | null | null |
2021.emnlp-main.688 | Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models | https://aclanthology.org/2021.emnlp-main.688/ | [
"Tassilo Klein",
"Moin Nabi"
] | Can we get existing language models and refine them for zero-shot commonsense reasoning? This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Schema Challenge by formulating the task as self-supervised refinement of a pre-trained language model. In contrast ... | 2021.emnlp-main.688 | 10.18653/v1/2021.emnlp-main.688 | null | 2109.05105 | title_snapshot |
2021.emnlp-main.689 | To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning | https://aclanthology.org/2021.emnlp-main.689/ | [
"Lukas Lange",
"Jannik Strötgen",
"Heike Adel",
"Dietrich Klakow"
] | In low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains. However, predicting useful transfer sources is a challenging problem, as even the most similar sources might lead to unexpected negative transfer results. Thus, ranking methods based on task and text similar... | 2021.emnlp-main.689 | 10.18653/v1/2021.emnlp-main.689 | null | 2104.08078 | title_snapshot |
2021.emnlp-main.690 | Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case | https://aclanthology.org/2021.emnlp-main.690/ | [
"Jingqing Zhang",
"Luis Bolanos Trujillo",
"Tong Li",
"Ashwani Tanwar",
"Guilherme Freire",
"Xian Yang",
"Julia Ive",
"Vibhor Gupta",
"Yike Guo"
] | Contextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remain supervised and underexploit the potential of the context. In this paper, we propose a self-supervised pre-training approach which is able ... | 2021.emnlp-main.690 | 10.18653/v1/2021.emnlp-main.690 | null | 2109.01935 | title_snapshot |
2021.emnlp-main.691 | ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring | https://aclanthology.org/2021.emnlp-main.691/ | [
"Vinay Aggarwal",
"Aparna Garimella",
"Balaji Vasan Srinivasan",
"Anandhavelu N",
"Rajiv Jain"
] | Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses calls for specific meth... | 2021.emnlp-main.691 | 10.18653/v1/2021.emnlp-main.691 | null | 2110.15794 | title_snapshot |
2021.emnlp-main.692 | Finnish Dialect Identification: The Effect of Audio and Text | https://aclanthology.org/2021.emnlp-main.692/ | [
"Mika Hämäläinen",
"Khalid Alnajjar",
"Niko Partanen",
"Jack Rueter"
] | Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recordi... | 2021.emnlp-main.692 | 10.18653/v1/2021.emnlp-main.692 | null | 2111.03800 | title_snapshot |
2021.emnlp-main.693 | English Machine Reading Comprehension Datasets: A Survey | https://aclanthology.org/2021.emnlp-main.693/ | [
"Daria Dzendzik",
"Jennifer Foster",
"Carl Vogel"
] | This paper surveys 60 English Machine Reading Comprehension datasets, with a view to providing a convenient resource for other researchers interested in this problem. We categorize the datasets according to their question and answer form and compare them across various dimensions including size, vocabulary, data source... | 2021.emnlp-main.693 | 10.18653/v1/2021.emnlp-main.693 | null | 2101.10421 | title_snapshot |
2021.emnlp-main.694 | Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection | https://aclanthology.org/2021.emnlp-main.694/ | [
"Priyanka Sen",
"Armin Oliya",
"Amir Saffari"
] | End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable. Previous implementations of this technique (Cohen et al, 2020) have focused on single-entity questions using a relation followin... | 2021.emnlp-main.694 | 10.18653/v1/2021.emnlp-main.694 | null | 2109.05808 | title_snapshot |
2021.emnlp-main.695 | Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs | https://aclanthology.org/2021.emnlp-main.695/ | [
"Pierre Marion",
"Pawel Nowak",
"Francesco Piccinno"
] | We tackle the problem of weakly-supervised conversational Question Answering over large Knowledge Graphs using a neural semantic parsing approach. We introduce a new Logical Form (LF) grammar that can model a wide range of queries on the graph while remaining sufficiently simple to generate supervision data efficiently... | 2021.emnlp-main.695 | 10.18653/v1/2021.emnlp-main.695 | null | 2109.00269 | title_snapshot |
2021.emnlp-main.696 | Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation | https://aclanthology.org/2021.emnlp-main.696/ | [
"Max Bartolo",
"Tristan Thrush",
"Robin Jia",
"Sebastian Riedel",
"Pontus Stenetorp",
"Douwe Kiela"
] | Despite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that fool a model-in-the-loop, can improve model robustness, this process is expensive which limits the... | 2021.emnlp-main.696 | 10.18653/v1/2021.emnlp-main.696 | null | 2104.08678 | title_snapshot |
2021.emnlp-main.697 | BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief | https://aclanthology.org/2021.emnlp-main.697/ | [
"Nora Kassner",
"Oyvind Tafjord",
"Hinrich Schütze",
"Peter Clark"
] | Although pretrained language models (PTLMs) contain significant amounts of world knowledge, they can still produce inconsistent answers to questions when probed, even after specialized training. As a result, it can be hard to identify what the model actually “believes” about the world, making it susceptible to inconsis... | 2021.emnlp-main.697 | 10.18653/v1/2021.emnlp-main.697 | null | 2109.14723 | title_snapshot |
2021.emnlp-main.698 | MLEC-QA: A Chinese Multi-Choice Biomedical Question Answering Dataset | https://aclanthology.org/2021.emnlp-main.698/ | [
"Jing Li",
"Shangping Zhong",
"Kaizhi Chen"
] | Question Answering (QA) has been successfully applied in scenarios of human-computer interaction such as chatbots and search engines. However, for the specific biomedical domain, QA systems are still immature due to expert-annotated datasets being limited by category and scale. In this paper, we present MLEC-QA, the la... | 2021.emnlp-main.698 | 10.18653/v1/2021.emnlp-main.698 | null | null | null |
2021.emnlp-main.699 | IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation | https://aclanthology.org/2021.emnlp-main.699/ | [
"Samuel Cahyawijaya",
"Genta Indra Winata",
"Bryan Wilie",
"Karissa Vincentio",
"Xiaohong Li",
"Adhiguna Kuncoro",
"Sebastian Ruder",
"Zhi Yuan Lim",
"Syafri Bahar",
"Masayu Khodra",
"Ayu Purwarianti",
"Pascale Fung"
] | Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of d... | 2021.emnlp-main.699 | 10.18653/v1/2021.emnlp-main.699 | null | 2104.08200 | title_snapshot |
2021.emnlp-main.700 | Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability | https://aclanthology.org/2021.emnlp-main.700/ | [
"Xin Lv",
"Yixin Cao",
"Lei Hou",
"Juanzi Li",
"Zhiyuan Liu",
"Yichi Zhang",
"Zelin Dai"
] | Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are actually unreasonable, while little work has been done on interpretability evaluation for them. In this paper, we propose a unified framewor... | 2021.emnlp-main.700 | 10.18653/v1/2021.emnlp-main.700 | null | 2104.06751 | title_snapshot |
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