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.401 | SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations | https://aclanthology.org/2021.emnlp-main.401/ | [
"Satwik Kottur",
"Seungwhan Moon",
"Alborz Geramifard",
"Babak Damavandi"
] | Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment. Existing task-oriented dialog datasets aimed towards virtual assistance fall short and do not situate the dialog in the user’s mu... | 2021.emnlp-main.401 | 10.18653/v1/2021.emnlp-main.401 | null | 2104.08667 | title_snapshot |
2021.emnlp-main.402 | RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging | https://aclanthology.org/2021.emnlp-main.402/ | [
"Jie Hao",
"Linfeng Song",
"Liwei Wang",
"Kun Xu",
"Zhaopeng Tu",
"Dong Yu"
] | The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset. We address this robustness i... | 2021.emnlp-main.402 | 10.18653/v1/2021.emnlp-main.402 | null | 2012.14535 | title_judge |
2021.emnlp-main.403 | MRF-Chat: Improving Dialogue with Markov Random Fields | https://aclanthology.org/2021.emnlp-main.403/ | [
"Ishaan Grover",
"Matthew Huggins",
"Cynthia Breazeal",
"Hae Won Park"
] | Recent state-of-the-art approaches in open-domain dialogue include training end-to-end deep-learning models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts in a knowledge graph and persona of the agent and the user, among others. While neural models... | 2021.emnlp-main.403 | 10.18653/v1/2021.emnlp-main.403 | null | null | null |
2021.emnlp-main.404 | Dialogue State Tracking with a Language Model using Schema-Driven Prompting | https://aclanthology.org/2021.emnlp-main.404/ | [
"Chia-Hsuan Lee",
"Hao Cheng",
"Mari Ostendorf"
] | Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using ... | 2021.emnlp-main.404 | 10.18653/v1/2021.emnlp-main.404 | null | 2109.07506 | title_snapshot |
2021.emnlp-main.405 | Signed Coreference Resolution | https://aclanthology.org/2021.emnlp-main.405/ | [
"Kayo Yin",
"Kenneth DeHaan",
"Malihe Alikhani"
] | Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing. In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed languages would not only enable higher-level Sign Language Processing... | 2021.emnlp-main.405 | 10.18653/v1/2021.emnlp-main.405 | null | null | null |
2021.emnlp-main.406 | Consistent Accelerated Inference via Confident Adaptive Transformers | https://aclanthology.org/2021.emnlp-main.406/ | [
"Tal Schuster",
"Adam Fisch",
"Tommi Jaakkola",
"Regina Barzilay"
] | We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP). Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs. In this work, we pres... | 2021.emnlp-main.406 | 10.18653/v1/2021.emnlp-main.406 | null | 2104.08803 | title_snapshot |
2021.emnlp-main.407 | Improving and Simplifying Pattern Exploiting Training | https://aclanthology.org/2021.emnlp-main.407/ | [
"Derek Tam",
"Rakesh R. Menon",
"Mohit Bansal",
"Shashank Srivastava",
"Colin Raffel"
] | Recently, pre-trained language models (LMs) have achieved strong performance when fine-tuned on difficult benchmarks like SuperGLUE. However, performance can suffer when there are very few labeled examples available for fine-tuning. Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-... | 2021.emnlp-main.407 | 10.18653/v1/2021.emnlp-main.407 | null | 2103.11955 | title_snapshot |
2021.emnlp-main.408 | Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data | https://aclanthology.org/2021.emnlp-main.408/ | [
"David Lowell",
"Brian Howard",
"Zachary C. Lipton",
"Byron Wallace"
] | Unsupervised Data Augmentation (UDA) is a semisupervised technique that applies a consistency loss to penalize differences between a model’s predictions on (a) observed (unlabeled) examples; and (b) corresponding ‘noised’ examples produced via data augmentation. While UDA has gained popularity for text classification, ... | 2021.emnlp-main.408 | 10.18653/v1/2021.emnlp-main.408 | null | 2010.11966 | title_snapshot |
2021.emnlp-main.409 | Pre-train or Annotate? Domain Adaptation with a Constrained Budget | https://aclanthology.org/2021.emnlp-main.409/ | [
"Fan Bai",
"Alan Ritter",
"Wei Xu"
] | Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance? In this paper, we study dom... | 2021.emnlp-main.409 | 10.18653/v1/2021.emnlp-main.409 | null | 2109.04711 | title_snapshot |
2021.emnlp-main.410 | Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources | https://aclanthology.org/2021.emnlp-main.410/ | [
"Ninareh Mehrabi",
"Pei Zhou",
"Fred Morstatter",
"Jay Pujara",
"Xiang Ren",
"Aram Galstyan"
] | Warning: this paper contains content that may be offensive or upsetting. Commonsense knowledge bases (CSKB) are increasingly used for various natural language processing tasks. Since CSKBs are mostly human-generated and may reflect societal biases, it is important to ensure that such biases are not conflated with the n... | 2021.emnlp-main.410 | 10.18653/v1/2021.emnlp-main.410 | null | 2103.11320 | title_snapshot |
2021.emnlp-main.411 | OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings | https://aclanthology.org/2021.emnlp-main.411/ | [
"Sunipa Dev",
"Tao Li",
"Jeff M Phillips",
"Vivek Srikumar"
] | Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks. While existing methods are effective at mitigating biases by linear projection, such methods are too aggressive: they not only remove bias, but also erase valuable information from word emb... | 2021.emnlp-main.411 | 10.18653/v1/2021.emnlp-main.411 | null | 2007.00049 | title_snapshot |
2021.emnlp-main.412 | Sentence-Permuted Paragraph Generation | https://aclanthology.org/2021.emnlp-main.412/ | [
"Wenhao Yu",
"Chenguang Zhu",
"Tong Zhao",
"Zhichun Guo",
"Meng Jiang"
] | Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-right sentence order. Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph. We propose a n... | 2021.emnlp-main.412 | 10.18653/v1/2021.emnlp-main.412 | null | 2104.07228 | title_snapshot |
2021.emnlp-main.413 | Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation | https://aclanthology.org/2021.emnlp-main.413/ | [
"Yuning Mao",
"Wenchang Ma",
"Deren Lei",
"Jiawei Han",
"Xiang Ren"
] | Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, with only the parallel training data and no additional guidance. However, it remains unclear whether current models can preserve important conc... | 2021.emnlp-main.413 | 10.18653/v1/2021.emnlp-main.413 | null | 2104.08724 | title_snapshot |
2021.emnlp-main.414 | Paraphrase Generation: A Survey of the State of the Art | https://aclanthology.org/2021.emnlp-main.414/ | [
"Jianing Zhou",
"Suma Bhat"
] | This paper focuses on paraphrase generation,which is a widely studied natural language generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years. This has provided architectures for contextualized representation of a... | 2021.emnlp-main.414 | 10.18653/v1/2021.emnlp-main.414 | null | null | null |
2021.emnlp-main.415 | Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? | https://aclanthology.org/2021.emnlp-main.415/ | [
"Tianxing He",
"Jingzhao Zhang",
"Zhiming Zhou",
"James Glass"
] | Exposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy. Although a lot of algorithms have been proposed to avoid teacher forcing and theref... | 2021.emnlp-main.415 | 10.18653/v1/2021.emnlp-main.415 | null | 1905.10617 | title_snapshot |
2021.emnlp-main.416 | Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning | https://aclanthology.org/2021.emnlp-main.416/ | [
"Li Zhou",
"Kevin Small",
"Yong Zhang",
"Sandeep Atluri"
] | Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with ques... | 2021.emnlp-main.416 | 10.18653/v1/2021.emnlp-main.416 | null | 2109.04689 | title_snapshot |
2021.emnlp-main.417 | Unsupervised Paraphrasing with Pretrained Language Models | https://aclanthology.org/2021.emnlp-main.417/ | [
"Tong Niu",
"Semih Yavuz",
"Yingbo Zhou",
"Nitish Shirish Keskar",
"Huan Wang",
"Caiming Xiong"
] | Paraphrase generation has benefited extensively from recent progress in the designing of training objectives and model architectures. However, previous explorations have largely focused on supervised methods, which require a large amount of labeled data that is costly to collect. To address this drawback, we adopt a tr... | 2021.emnlp-main.417 | 10.18653/v1/2021.emnlp-main.417 | null | 2010.12885 | title_snapshot |
2021.emnlp-main.418 | Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness | https://aclanthology.org/2021.emnlp-main.418/ | [
"Hengtong Zhang",
"Tianhang Zheng",
"Yaliang Li",
"Jing Gao",
"Lu Su",
"Bo Li"
] | Seq2seq models have demonstrated their incredible effectiveness in a large variety of applications. However, recent research has shown that inappropriate language in training samples and well-designed testing cases can induce seq2seq models to output profanity. These outputs may potentially hurt the usability of seq2se... | 2021.emnlp-main.418 | 10.18653/v1/2021.emnlp-main.418 | null | null | null |
2021.emnlp-main.419 | Journalistic Guidelines Aware News Image Captioning | https://aclanthology.org/2021.emnlp-main.419/ | [
"Xuewen Yang",
"Svebor Karaman",
"Joel Tetreault",
"Alejandro Jaimes"
] | The task of news article image captioning aims to generate descriptive and informative captions for news article images. Unlike conventional image captions that simply describe the content of the image in general terms, news image captions follow journalistic guidelines and rely heavily on named entities to describe th... | 2021.emnlp-main.419 | 10.18653/v1/2021.emnlp-main.419 | null | 2109.02865 | title_snapshot |
2021.emnlp-main.420 | AESOP: Paraphrase Generation with Adaptive Syntactic Control | https://aclanthology.org/2021.emnlp-main.420/ | [
"Jiao Sun",
"Xuezhe Ma",
"Nanyun Peng"
] | We propose to control paraphrase generation through carefully chosen target syntactic structures to generate more proper and higher quality paraphrases. Our model, AESOP, leverages a pretrained language model and adds deliberately chosen syntactical control via a retrieval-based selection module to generate fluent para... | 2021.emnlp-main.420 | 10.18653/v1/2021.emnlp-main.420 | null | null | null |
2021.emnlp-main.421 | Refocusing on Relevance: Personalization in NLG | https://aclanthology.org/2021.emnlp-main.421/ | [
"Shiran Dudy",
"Steven Bedrick",
"Bonnie Webber"
] | Many NLG tasks such as summarization, dialogue response, or open domain question answering, focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user’s intent or context of work is not easily recoverable based solely on that source text– a scenario... | 2021.emnlp-main.421 | 10.18653/v1/2021.emnlp-main.421 | null | 2109.05140 | title_snapshot |
2021.emnlp-main.422 | The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction | https://aclanthology.org/2021.emnlp-main.422/ | [
"Manling Li",
"Sha Li",
"Zhenhailong Wang",
"Lifu Huang",
"Kyunghyun Cho",
"Heng Ji",
"Jiawei Han",
"Clare Voss"
] | Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event schema induction focuses either on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and a... | 2021.emnlp-main.422 | 10.18653/v1/2021.emnlp-main.422 | null | 2104.06344 | title_snapshot |
2021.emnlp-main.423 | Learning Constraints and Descriptive Segmentation for Subevent Detection | https://aclanthology.org/2021.emnlp-main.423/ | [
"Haoyu Wang",
"Hongming Zhang",
"Muhao Chen",
"Dan Roth"
] | Event mentions in text correspond to real-world events of varying degrees of granularity. The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes. Since knowing the span of descriptive contexts of event complexes helps infer the membe... | 2021.emnlp-main.423 | 10.18653/v1/2021.emnlp-main.423 | null | 2109.06316 | title_snapshot |
2021.emnlp-main.424 | ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision | https://aclanthology.org/2021.emnlp-main.424/ | [
"Xuan Wang",
"Vivian Hu",
"Xiangchen Song",
"Shweta Garg",
"Jinfeng Xiao",
"Jiawei Han"
] | Scientific literature analysis needs fine-grained named entity recognition (NER) to provide a wide range of information for scientific discovery. For example, chemistry research needs to study dozens to hundreds of distinct, fine-grained entity types, making consistent and accurate annotation difficult even for crowds ... | 2021.emnlp-main.424 | 10.18653/v1/2021.emnlp-main.424 | null | null | null |
2021.emnlp-main.425 | Moving on from OntoNotes: Coreference Resolution Model Transfer | https://aclanthology.org/2021.emnlp-main.425/ | [
"Patrick Xia",
"Benjamin Van Durme"
] | Academic neural models for coreference resolution (coref) are typically trained on a single dataset, OntoNotes, and model improvements are benchmarked on that same dataset. However, real-world applications of coref depend on the annotation guidelines and the domain of the target dataset, which often differ from those o... | 2021.emnlp-main.425 | 10.18653/v1/2021.emnlp-main.425 | null | 2104.08457 | title_snapshot |
2021.emnlp-main.426 | Document-level Entity-based Extraction as Template Generation | https://aclanthology.org/2021.emnlp-main.426/ | [
"Kung-Hsiang Huang",
"Sam Tang",
"Nanyun Peng"
] | Document-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains. Most document-level EE systems build extractive models, which struggle to model long-term dependencies ... | 2021.emnlp-main.426 | 10.18653/v1/2021.emnlp-main.426 | null | 2109.04901 | title_snapshot |
2021.emnlp-main.427 | Learning Prototype Representations Across Few-Shot Tasks for Event Detection | https://aclanthology.org/2021.emnlp-main.427/ | [
"Viet Lai",
"Franck Dernoncourt",
"Thien Huu Nguyen"
] | We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. We further propose to enforce prediction consistency among classifie... | 2021.emnlp-main.427 | 10.18653/v1/2021.emnlp-main.427 | null | null | null |
2021.emnlp-main.428 | Lifelong Event Detection with Knowledge Transfer | https://aclanthology.org/2021.emnlp-main.428/ | [
"Pengfei Yu",
"Heng Ji",
"Prem Natarajan"
] | Traditional supervised Information Extraction (IE) methods can extract structured knowledge elements from unstructured data, but it is limited to a pre-defined target ontology. In reality, the ontology of interest may change over time, adding emergent new types or more fine-grained subtypes. We propose a new lifelong l... | 2021.emnlp-main.428 | 10.18653/v1/2021.emnlp-main.428 | null | null | null |
2021.emnlp-main.429 | Modular Self-Supervision for Document-Level Relation Extraction | https://aclanthology.org/2021.emnlp-main.429/ | [
"Sheng Zhang",
"Cliff Wong",
"Naoto Usuyama",
"Sarthak Jain",
"Tristan Naumann",
"Hoifung Poon"
] | Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications. Compared to conventional information extraction confined to short ... | 2021.emnlp-main.429 | 10.18653/v1/2021.emnlp-main.429 | null | 2109.05362 | title_snapshot |
2021.emnlp-main.430 | Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition | https://aclanthology.org/2021.emnlp-main.430/ | [
"Rui Wang",
"Ricardo Henao"
] | Unsupervised consistency training is a way of semi-supervised learning that encourages consistency in model predictions between the original and augmented data. For Named Entity Recognition (NER), existing approaches augment the input sequence with token replacement, assuming annotations on the replaced positions uncha... | 2021.emnlp-main.430 | 10.18653/v1/2021.emnlp-main.430 | null | null | null |
2021.emnlp-main.431 | Fine-grained Entity Typing without Knowledge Base | https://aclanthology.org/2021.emnlp-main.431/ | [
"Jing Qian",
"Yibin Liu",
"Lemao Liu",
"Yangming Li",
"Haiyun Jiang",
"Haisong Zhang",
"Shuming Shi"
] | Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision. However, the reliance on KB means this training setting can be hampered by the lack of or the incompleteness of the KB. To alleviate this limitation, we prop... | 2021.emnlp-main.431 | 10.18653/v1/2021.emnlp-main.431 | null | null | null |
2021.emnlp-main.432 | Adversarial Attack against Cross-lingual Knowledge Graph Alignment | https://aclanthology.org/2021.emnlp-main.432/ | [
"Zeru Zhang",
"Zijie Zhang",
"Yang Zhou",
"Lingfei Wu",
"Sixing Wu",
"Xiaoying Han",
"Dejing Dou",
"Tianshi Che",
"Da Yan"
] | Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of cross-lingual entity alignment under adversarial attacks. This paper proposes an adversarial attack model with two novel attack techniques ... | 2021.emnlp-main.432 | 10.18653/v1/2021.emnlp-main.432 | null | null | null |
2021.emnlp-main.433 | Towards Realistic Few-Shot Relation Extraction | https://aclanthology.org/2021.emnlp-main.433/ | [
"Sam Brody",
"Sichao Wu",
"Adrian Benton"
] | In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification... | 2021.emnlp-main.433 | 10.18653/v1/2021.emnlp-main.433 | null | null | null |
2021.emnlp-main.434 | Data Augmentation for Cross-Domain Named Entity Recognition | https://aclanthology.org/2021.emnlp-main.434/ | [
"Shuguang Chen",
"Gustavo Aguilar",
"Leonardo Neves",
"Thamar Solorio"
] | Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limited. In this work, we take this research direction to the opposite and s... | 2021.emnlp-main.434 | 10.18653/v1/2021.emnlp-main.434 | null | 2109.01758 | title_snapshot |
2021.emnlp-main.435 | Incorporating medical knowledge in BERT for clinical relation extraction | https://aclanthology.org/2021.emnlp-main.435/ | [
"Arpita Roy",
"Shimei Pan"
] | In recent years pre-trained language models (PLM) such as BERT have proven to be very effective in diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question Answering. Trained with massive general-domain text, these pre-trained language models capture rich syntactic, semantic and discourse infor... | 2021.emnlp-main.435 | 10.18653/v1/2021.emnlp-main.435 | null | null | null |
2021.emnlp-main.436 | ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning | https://aclanthology.org/2021.emnlp-main.436/ | [
"Rujun Han",
"Xiang Ren",
"Nanyun Peng"
] | While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications. We present a continual pre-training approach that equips PTLMs with targeted knowledge about event tempora... | 2021.emnlp-main.436 | 10.18653/v1/2021.emnlp-main.436 | null | 2012.15283 | title_snapshot |
2021.emnlp-main.437 | Learning from Noisy Labels for Entity-Centric Information Extraction | https://aclanthology.org/2021.emnlp-main.437/ | [
"Wenxuan Zhou",
"Muhao Chen"
] | Recent information extraction approaches have relied on training deep neural models. However, such models can easily overfit noisy labels and suffer from performance degradation. While it is very costly to filter noisy labels in large learning resources, recent studies show that such labels take more training steps to ... | 2021.emnlp-main.437 | 10.18653/v1/2021.emnlp-main.437 | null | 2104.08656 | title_snapshot |
2021.emnlp-main.438 | Extracting Material Property Measurement Data from Scientific Articles | https://aclanthology.org/2021.emnlp-main.438/ | [
"Gihan Panapitiya",
"Fred Parks",
"Jonathan Sepulveda",
"Emily Saldanha"
] | Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training data sets. The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these data is essential to support the development of reliable property pre... | 2021.emnlp-main.438 | 10.18653/v1/2021.emnlp-main.438 | null | null | null |
2021.emnlp-main.439 | Modeling Document-Level Context for Event Detection via Important Context Selection | https://aclanthology.org/2021.emnlp-main.439/ | [
"Amir Pouran Ben Veyseh",
"Minh Van Nguyen",
"Nghia Ngo Trung",
"Bonan Min",
"Thien Huu Nguyen"
] | The task of Event Detection (ED) in Information Extraction aims to recognize and classify trigger words of events in text. The recent progress has featured advanced transformer-based language models (e.g., BERT) as a critical component in state-of-the-art models for ED. However, the length limit for input texts is a ba... | 2021.emnlp-main.439 | 10.18653/v1/2021.emnlp-main.439 | null | null | null |
2021.emnlp-main.440 | Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments | https://aclanthology.org/2021.emnlp-main.440/ | [
"Minh Van Nguyen",
"Tuan Ngo Nguyen",
"Bonan Min",
"Thien Huu Nguyen"
] | Previous work on crosslingual Relation and Event Extraction (REE) suffers from the monolingual bias issue due to the training of models on only the source language data. An approach to overcome this issue is to use unlabeled data in the target language to aid the alignment of crosslingual representations, i.e., via foo... | 2021.emnlp-main.440 | 10.18653/v1/2021.emnlp-main.440 | null | null | null |
2021.emnlp-main.441 | Corpus-based Open-Domain Event Type Induction | https://aclanthology.org/2021.emnlp-main.441/ | [
"Jiaming Shen",
"Yunyi Zhang",
"Heng Ji",
"Jiawei Han"
] | Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents a corpus-based open-domain event type induction method that automatically discovers a set o... | 2021.emnlp-main.441 | 10.18653/v1/2021.emnlp-main.441 | null | 2109.03322 | title_snapshot |
2021.emnlp-main.442 | PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition | https://aclanthology.org/2021.emnlp-main.442/ | [
"Tao Zhang",
"Congying Xia",
"Philip S. Yu",
"Zhiwei Liu",
"Shu Zhao"
] | Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain. Due to limited labeled resources and domain shift, cross-domain NER is a challenging task. To address these challenges, we propose a progressive domain adaptation Knowledge Distillation ... | 2021.emnlp-main.442 | 10.18653/v1/2021.emnlp-main.442 | null | null | null |
2021.emnlp-main.443 | Multi-Vector Attention Models for Deep Re-ranking | https://aclanthology.org/2021.emnlp-main.443/ | [
"Giulio Zhou",
"Jacob Devlin"
] | Large-scale document retrieval systems often utilize two styles of neural network models which live at two different ends of the joint computation vs. accuracy spectrum. The first style is dual encoder (or two-tower) models, where the query and document representations are computed completely independently and combined... | 2021.emnlp-main.443 | 10.18653/v1/2021.emnlp-main.443 | null | null | null |
2021.emnlp-main.444 | Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy | https://aclanthology.org/2021.emnlp-main.444/ | [
"Maharshi Gor",
"Kellie Webster",
"Jordan Boyd-Graber"
] | The goal of question answering (QA) is to answer _any_ question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, an analysis of model accuracy reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is mor... | 2021.emnlp-main.444 | 10.18653/v1/2021.emnlp-main.444 | null | 2104.07571 | title_judge |
2021.emnlp-main.445 | Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models | https://aclanthology.org/2021.emnlp-main.445/ | [
"Kaixin Ma",
"Filip Ilievski",
"Jonathan Francis",
"Satoru Ozaki",
"Eric Nyberg",
"Alessandro Oltramari"
] | Commonsense reasoning benchmarks have been largely solved by fine-tuning language models. The downside is that fine-tuning may cause models to overfit to task-specific data and thereby forget their knowledge gained during pre-training. Recent works only propose lightweight model updates as models may already possess us... | 2021.emnlp-main.445 | 10.18653/v1/2021.emnlp-main.445 | null | 2109.02837 | title_snapshot |
2021.emnlp-main.446 | Transformer Feed-Forward Layers Are Key-Value Memories | https://aclanthology.org/2021.emnlp-main.446/ | [
"Mor Geva",
"Roei Schuster",
"Jonathan Berant",
"Omer Levy"
] | Feed-forward layers constitute two-thirds of a transformer model’s parameters, yet their role in the network remains under-explored. We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each val... | 2021.emnlp-main.446 | 10.18653/v1/2021.emnlp-main.446 | null | 2012.14913 | title_snapshot |
2021.emnlp-main.447 | Connecting Attributions and QA Model Behavior on Realistic Counterfactuals | https://aclanthology.org/2021.emnlp-main.447/ | [
"Xi Ye",
"Rohan Nair",
"Greg Durrett"
] | When a model attribution technique highlights a particular part of the input, a user might understand this highlight as making a statement about counterfactuals (Miller, 2019): if that part of the input were to change, the model’s prediction might change as well. This paper investigates how well different attribution t... | 2021.emnlp-main.447 | 10.18653/v1/2021.emnlp-main.447 | null | 2104.04515 | title_snapshot |
2021.emnlp-main.448 | How Do Neural Sequence Models Generalize? Local and Global Cues for Out-of-Distribution Prediction | https://aclanthology.org/2021.emnlp-main.448/ | [
"D. Anthony Bau",
"Jacob Andreas"
] | After a neural sequence model encounters an unexpected token, can its behavior be predicted? We show that RNN and transformer language models exhibit structured, consistent generalization in out-of-distribution contexts. We begin by introducing two idealized models of generalization in next-word prediction: a lexical c... | 2021.emnlp-main.448 | 10.18653/v1/2021.emnlp-main.448 | null | 2111.03108 | title_judge |
2021.emnlp-main.449 | Comparing Text Representations: A Theory-Driven Approach | https://aclanthology.org/2021.emnlp-main.449/ | [
"Gregory Yauney",
"David Mimno"
] | Much of the progress in contemporary NLP has come from learning representations, such as masked language model (MLM) contextual embeddings, that turn challenging problems into simple classification tasks. But how do we quantify and explain this effect? We adapt general tools from computational learning theory to fit th... | 2021.emnlp-main.449 | 10.18653/v1/2021.emnlp-main.449 | null | 2109.07458 | title_snapshot |
2021.emnlp-main.450 | Human Rationales as Attribution Priors for Explainable Stance Detection | https://aclanthology.org/2021.emnlp-main.450/ | [
"Sahil Jayaram",
"Emily Allaway"
] | As NLP systems become better at detecting opinions and beliefs from text, it is important to ensure not only that models are accurate but also that they arrive at their predictions in ways that align with human reasoning. In this work, we present a method for imparting human-like rationalization to a stance detection m... | 2021.emnlp-main.450 | 10.18653/v1/2021.emnlp-main.450 | null | null | null |
2021.emnlp-main.451 | The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders | https://aclanthology.org/2021.emnlp-main.451/ | [
"Han He",
"Jinho D. Choi"
] | Multi-task learning with transformer encoders (MTL) has emerged as a powerful technique to improve performance on closely-related tasks for both accuracy and efficiency while a question still remains whether or not it would perform as well on tasks that are distinct in nature. We first present MTL results on five NLP t... | 2021.emnlp-main.451 | 10.18653/v1/2021.emnlp-main.451 | null | 2109.06939 | title_snapshot |
2021.emnlp-main.452 | Text Counterfactuals via Latent Optimization and Shapley-Guided Search | https://aclanthology.org/2021.emnlp-main.452/ | [
"Xiaoli Fern",
"Quintin Pope"
] | We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. Given a textual input and a classification model, we aim to minimally alter the text to change the model’s prediction. White-box approaches have been successfully applied to similar problem... | 2021.emnlp-main.452 | 10.18653/v1/2021.emnlp-main.452 | null | 2110.11589 | title_snapshot |
2021.emnlp-main.453 | “Average” Approximates “First Principal Component”? An Empirical Analysis on Representations from Neural Language Models | https://aclanthology.org/2021.emnlp-main.453/ | [
"Zihan Wang",
"Chengyu Dong",
"Jingbo Shang"
] | Contextualized representations based on neural language models have furthered the state of the art in various NLP tasks. Despite its great success, the nature of such representations remains a mystery. In this paper, we present an empirical property of these representations—”average” approximates “first principal compo... | 2021.emnlp-main.453 | 10.18653/v1/2021.emnlp-main.453 | null | 2104.08673 | title_snapshot |
2021.emnlp-main.454 | Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models | https://aclanthology.org/2021.emnlp-main.454/ | [
"Yiwen Wang",
"Jennifer Hu",
"Roger Levy",
"Peng Qian"
] | Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement. However, it remains unclear if such an inductive bias would also improve language models’ ability to learn grammatical dependencies in typologically different la... | 2021.emnlp-main.454 | 10.18653/v1/2021.emnlp-main.454 | null | 2109.11058 | title_snapshot |
2021.emnlp-main.455 | GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks | https://aclanthology.org/2021.emnlp-main.455/ | [
"Weicheng Ma",
"Renze Lou",
"Kai Zhang",
"Lili Wang",
"Soroush Vosoughi"
] | A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. This paper presents GradTS, an automatic auxiliary task selection method based on gradient calculation in Transformer-based models. Compared to AUTOSEM, a strong baseline method, GradTS improves the performa... | 2021.emnlp-main.455 | 10.18653/v1/2021.emnlp-main.455 | null | 2109.05748 | title_snapshot |
2021.emnlp-main.456 | NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases | https://aclanthology.org/2021.emnlp-main.456/ | [
"Tara Safavi",
"Jing Zhu",
"Danai Koutra"
] | Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence. Recently, much progress toward this goal has been made with automatic knowledge base (KB) construction techniques. However, such techniques focus primarily on the acquisition of positive (true) KB statements, even though nega... | 2021.emnlp-main.456 | 10.18653/v1/2021.emnlp-main.456 | null | 2011.07497 | title_snapshot |
2021.emnlp-main.457 | Instance-adaptive training with noise-robust losses against noisy labels | https://aclanthology.org/2021.emnlp-main.457/ | [
"Lifeng Jin",
"Linfeng Song",
"Kun Xu",
"Dong Yu"
] | In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data. However, model training with such datasets poses a challenge because popular optimization objectives are not robust to label ... | 2021.emnlp-main.457 | 10.18653/v1/2021.emnlp-main.457 | null | null | null |
2021.emnlp-main.458 | Distributionally Robust Multilingual Machine Translation | https://aclanthology.org/2021.emnlp-main.458/ | [
"Chunting Zhou",
"Daniel Levy",
"Xian Li",
"Marjan Ghazvininejad",
"Graham Neubig"
] | Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of deployed models. However, the heavy data imbalance between languages hinders the model from performing uniformly across language pairs. In ... | 2021.emnlp-main.458 | 10.18653/v1/2021.emnlp-main.458 | null | 2109.04020 | title_snapshot |
2021.emnlp-main.459 | Model Selection for Cross-lingual Transfer | https://aclanthology.org/2021.emnlp-main.459/ | [
"Yang Chen",
"Alan Ritter"
] | Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities. In the zero-shot transfer setting, only English training data is used, and the fine-tuned model is evaluated on another target language. While this works surprisingly ... | 2021.emnlp-main.459 | 10.18653/v1/2021.emnlp-main.459 | null | 2010.06127 | title_snapshot |
2021.emnlp-main.460 | Continual Few-Shot Learning for Text Classification | https://aclanthology.org/2021.emnlp-main.460/ | [
"Ramakanth Pasunuru",
"Veselin Stoyanov",
"Mohit Bansal"
] | Natural Language Processing (NLP) is increasingly relying on general end-to-end systems that need to handle many different linguistic phenomena and nuances. For example, a Natural Language Inference (NLI) system has to recognize sentiment, handle numbers, perform coreference, etc. Our solutions to complex problems are ... | 2021.emnlp-main.460 | 10.18653/v1/2021.emnlp-main.460 | null | null | null |
2021.emnlp-main.461 | Efficient Nearest Neighbor Language Models | https://aclanthology.org/2021.emnlp-main.461/ | [
"Junxian He",
"Graham Neubig",
"Taylor Berg-Kirkpatrick"
] | Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints. While effective, these models often require retrieval from a large datastore at test time, significantly increasing the i... | 2021.emnlp-main.461 | 10.18653/v1/2021.emnlp-main.461 | null | 2109.04212 | title_snapshot |
2021.emnlp-main.462 | STraTA: Self-Training with Task Augmentation for Better Few-shot Learning | https://aclanthology.org/2021.emnlp-main.462/ | [
"Tu Vu",
"Minh-Thang Luong",
"Quoc Le",
"Grady Simon",
"Mohit Iyyer"
] | Despite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available. To address this shortcoming, we propose STraTA, which stands for Self-Training with Task Augmentation, an approach that ... | 2021.emnlp-main.462 | 10.18653/v1/2021.emnlp-main.462 | null | 2109.06270 | title_snapshot |
2021.emnlp-main.463 | TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection | https://aclanthology.org/2021.emnlp-main.463/ | [
"Vivek Madan",
"Ashish Khetan",
"Zohar Karnin"
] | The paradigm of pre-training followed by finetuning has become a standard procedure for NLP tasks, with a known problem of domain shift between the pre-training and downstream corpus. Previous works have tried to mitigate this problem with additional pre-training, either on the downstream corpus itself when it is large... | 2021.emnlp-main.463 | 10.18653/v1/2021.emnlp-main.463 | null | null | null |
2021.emnlp-main.464 | Gradient-based Adversarial Attacks against Text Transformers | https://aclanthology.org/2021.emnlp-main.464/ | [
"Chuan Guo",
"Alexandre Sablayrolles",
"Hervé Jégou",
"Douwe Kiela"
] | We propose the first general-purpose gradient-based adversarial attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization. We empirically demonstra... | 2021.emnlp-main.464 | 10.18653/v1/2021.emnlp-main.464 | null | 2104.13733 | title_snapshot |
2021.emnlp-main.465 | Do Transformer Modifications Transfer Across Implementations and Applications? | https://aclanthology.org/2021.emnlp-main.465/ | [
"Sharan Narang",
"Hyung Won Chung",
"Yi Tay",
"Liam Fedus",
"Thibault Fevry",
"Michael Matena",
"Karishma Malkan",
"Noah Fiedel",
"Noam Shazeer",
"Zhenzhong Lan",
"Yanqi Zhou",
"Wei Li",
"Nan Ding",
"Jake Marcus",
"Adam Roberts",
"Colin Raffel"
] | The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread adoption. In this paper, we comprehensively evaluate many of these modifications in a shared experimental setting that covers most of the c... | 2021.emnlp-main.465 | 10.18653/v1/2021.emnlp-main.465 | null | 2102.11972 | title_snapshot |
2021.emnlp-main.466 | Paired Examples as Indirect Supervision in Latent Decision Models | https://aclanthology.org/2021.emnlp-main.466/ | [
"Nitish Gupta",
"Sameer Singh",
"Matt Gardner",
"Dan Roth"
] | Compositional, structured models are appealing because they explicitly decompose problems and provide interpretable intermediate outputs that give confidence that the model is not simply latching onto data artifacts. Learning these models is challenging, however, because end-task supervision only provides a weak indire... | 2021.emnlp-main.466 | 10.18653/v1/2021.emnlp-main.466 | null | 2104.01759 | title_snapshot |
2021.emnlp-main.467 | Pairwise Supervised Contrastive Learning of Sentence Representations | https://aclanthology.org/2021.emnlp-main.467/ | [
"Dejiao Zhang",
"Shang-Wen Li",
"Wei Xiao",
"Henghui Zhu",
"Ramesh Nallapati",
"Andrew O. Arnold",
"Bing Xiang"
] | Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese loss. Nevertheless, they share a common weakness: sentences in a contradiction pair are not necessarily from different semantic categories. The... | 2021.emnlp-main.467 | 10.18653/v1/2021.emnlp-main.467 | null | 2109.05424 | title_snapshot |
2021.emnlp-main.468 | Muppet: Massive Multi-task Representations with Pre-Finetuning | https://aclanthology.org/2021.emnlp-main.468/ | [
"Armen Aghajanyan",
"Anchit Gupta",
"Akshat Shrivastava",
"Xilun Chen",
"Luke Zettlemoyer",
"Sonal Gupta"
] | We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many ... | 2021.emnlp-main.468 | 10.18653/v1/2021.emnlp-main.468 | null | 2101.11038 | title_snapshot |
2021.emnlp-main.469 | Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP | https://aclanthology.org/2021.emnlp-main.469/ | [
"Trapit Bansal",
"Karthick Prasad Gunasekaran",
"Tong Wang",
"Tsendsuren Munkhdalai",
"Andrew McCallum"
] | Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks. However, the efficacy of meta-learning crucially depends on the distribution of tasks available for training, and this is often assumed to be known a priori or constructed f... | 2021.emnlp-main.469 | 10.18653/v1/2021.emnlp-main.469 | null | 2111.01322 | title_snapshot |
2021.emnlp-main.470 | A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations | https://aclanthology.org/2021.emnlp-main.470/ | [
"Ziyi Yang",
"Yinfei Yang",
"Daniel Cer",
"Eric Darve"
] | Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models. We explore this problem from a novel angle of geometric algebra and semantic space. A simple but highly effective method “Language Information Removal (LIR)” factors out language iden... | 2021.emnlp-main.470 | 10.18653/v1/2021.emnlp-main.470 | null | 2109.04727 | title_snapshot |
2021.emnlp-main.471 | A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space | https://aclanthology.org/2021.emnlp-main.471/ | [
"Alexander Jones",
"William Yang Wang",
"Kyle Mahowald"
] | In cross-lingual language models, representations for many different languages live in the same space. Here, we investigate the linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs. Using BERT-based LaBSE and BiLS... | 2021.emnlp-main.471 | 10.18653/v1/2021.emnlp-main.471 | null | 2109.06324 | title_snapshot |
2021.emnlp-main.472 | Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing | https://aclanthology.org/2021.emnlp-main.472/ | [
"Jingfeng Yang",
"Federico Fancellu",
"Bonnie Webber",
"Diyi Yang"
] | The availability of corpora has led to significant advances in training semantic parsers in English. Unfortunately, for languages other than English, annotated data is limited and so is the performance of the developed parsers. Recently, pretrained multilingual models have been proven useful for zero-shot cross-lingual... | 2021.emnlp-main.472 | 10.18653/v1/2021.emnlp-main.472 | null | null | null |
2021.emnlp-main.473 | Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings | https://aclanthology.org/2021.emnlp-main.473/ | [
"Junkun Chen",
"Renjie Zheng",
"Atsuhito Kita",
"Mingbo Ma",
"Liang Huang"
] | Simultaneous translation is vastly different from full-sentence translation, in the sense that it starts translation before the source sentence ends, with only a few words delay. However, due to the lack of large-scale, high-quality simultaneous translation datasets, most such systems are still trained on conventional ... | 2021.emnlp-main.473 | 10.18653/v1/2021.emnlp-main.473 | null | 2010.11247 | title_snapshot |
2021.emnlp-main.474 | Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications | https://aclanthology.org/2021.emnlp-main.474/ | [
"Shuo Sun",
"Ahmed El-Kishky",
"Vishrav Chaudhary",
"James Cross",
"Lucia Specia",
"Francisco Guzmán"
] | Sentence-level Quality estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels. Recent QE models have achieved previously-unseen levels of correlation with human judgments, but they rely on l... | 2021.emnlp-main.474 | 10.18653/v1/2021.emnlp-main.474 | null | 2109.08627 | title_snapshot |
2021.emnlp-main.475 | A Large-Scale Study of Machine Translation in Turkic Languages | https://aclanthology.org/2021.emnlp-main.475/ | [
"Jamshidbek Mirzakhalov",
"Anoop Babu",
"Duygu Ataman",
"Sherzod Kariev",
"Francis Tyers",
"Otabek Abduraufov",
"Mammad Hajili",
"Sardana Ivanova",
"Abror Khaytbaev",
"Antonio Laverghetta Jr.",
"Bekhzodbek Moydinboyev",
"Esra Onal",
"Shaxnoza Pulatova",
"Ahsan Wahab",
"Orhan Firat",
"S... | Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first l... | 2021.emnlp-main.475 | 10.18653/v1/2021.emnlp-main.475 | null | null | null |
2021.emnlp-main.476 | Analyzing the Surprising Variability in Word Embedding Stability Across Languages | https://aclanthology.org/2021.emnlp-main.476/ | [
"Laura Burdick",
"Jonathan K. Kummerfeld",
"Rada Mihalcea"
] | Word embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages. To gain further insight into word embeddings, we explore their stability (e.g., overlap between the nearest neighbors of a word in different embedding spaces) ... | 2021.emnlp-main.476 | 10.18653/v1/2021.emnlp-main.476 | null | 2004.14876 | title_snapshot |
2021.emnlp-main.477 | Rule-based Morphological Inflection Improves Neural Terminology Translation | https://aclanthology.org/2021.emnlp-main.477/ | [
"Weijia Xu",
"Marine Carpuat"
] | Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms. This limits their application to real-world scenarios where constraint terms are provided as lemmas. In this paper, we introduce a modular... | 2021.emnlp-main.477 | 10.18653/v1/2021.emnlp-main.477 | null | 2109.04620 | title_snapshot |
2021.emnlp-main.478 | Data and Parameter Scaling Laws for Neural Machine Translation | https://aclanthology.org/2021.emnlp-main.478/ | [
"Mitchell A Gordon",
"Kevin Duh",
"Jared Kaplan"
] | We observe that the development cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model. We discuss some practical implications of these results, such as predicting BLEU achieved by large scale mo... | 2021.emnlp-main.478 | 10.18653/v1/2021.emnlp-main.478 | null | null | null |
2021.emnlp-main.479 | Good-Enough Example Extrapolation | https://aclanthology.org/2021.emnlp-main.479/ | [
"Jason Wei"
] | This paper asks whether extrapolating the hidden space distribution of text examples from one class onto another is a valid inductive bias for data augmentation. To operationalize this question, I propose a simple data augmentation protocol called “good-enough example extrapolation” (GE3). GE3 is lightweight and has no... | 2021.emnlp-main.479 | 10.18653/v1/2021.emnlp-main.479 | null | 2109.05602 | title_snapshot |
2021.emnlp-main.480 | Learning to Selectively Learn for Weakly-supervised Paraphrase Generation | https://aclanthology.org/2021.emnlp-main.480/ | [
"Kaize Ding",
"Dingcheng Li",
"Alexander Hanbo Li",
"Xing Fan",
"Chenlei Guo",
"Yang Liu",
"Huan Liu"
] | Paraphrase generation is a longstanding NLP task that has diverse applications on downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to alleviate this issue, they may fail to generate meaning... | 2021.emnlp-main.480 | 10.18653/v1/2021.emnlp-main.480 | null | 2109.12457 | title_snapshot |
2021.emnlp-main.481 | Effective Convolutional Attention Network for Multi-label Clinical Document Classification | https://aclanthology.org/2021.emnlp-main.481/ | [
"Yang Liu",
"Hua Cheng",
"Russell Klopfer",
"Matthew R. Gormley",
"Thomas Schaaf"
] | Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documen... | 2021.emnlp-main.481 | 10.18653/v1/2021.emnlp-main.481 | null | null | null |
2021.emnlp-main.482 | Contrastive Code Representation Learning | https://aclanthology.org/2021.emnlp-main.482/ | [
"Paras Jain",
"Ajay Jain",
"Tianjun Zhang",
"Pieter Abbeel",
"Joseph Gonzalez",
"Ion Stoica"
] | Recent work learns contextual representations of source code by reconstructing tokens from their context. For downstream semantic understanding tasks like code clone detection, these representations should ideally capture program functionality. However, we show that the popular reconstruction-based RoBERTa model is sen... | 2021.emnlp-main.482 | 10.18653/v1/2021.emnlp-main.482 | null | 2007.04973 | title_snapshot |
2021.emnlp-main.483 | IGA: An Intent-Guided Authoring Assistant | https://aclanthology.org/2021.emnlp-main.483/ | [
"Simeng Sun",
"Wenlong Zhao",
"Varun Manjunatha",
"Rajiv Jain",
"Vlad Morariu",
"Franck Dernoncourt",
"Balaji Vasan Srinivasan",
"Mohit Iyyer"
] | While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored. We leverage advances in language modeling to build an interactive writing assistant that generates and rephrases tex... | 2021.emnlp-main.483 | 10.18653/v1/2021.emnlp-main.483 | null | 2104.07000 | title_snapshot |
2021.emnlp-main.484 | Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints | https://aclanthology.org/2021.emnlp-main.484/ | [
"Zichao Wang",
"Andrew Lan",
"Richard Baraniuk"
] | We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. Th... | 2021.emnlp-main.484 | 10.18653/v1/2021.emnlp-main.484 | null | 2109.04546 | title_snapshot |
2021.emnlp-main.485 | Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning | https://aclanthology.org/2021.emnlp-main.485/ | [
"Yuanzhi Chen",
"Mohammad Hasan"
] | Irrespective of the success of the deep learning-based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from COVID-19 social media data. Due to the inherent complexity of this type of data, caused by its... | 2021.emnlp-main.485 | 10.18653/v1/2021.emnlp-main.485 | null | 2110.15703 | title_snapshot |
2021.emnlp-main.486 | Detecting Health Advice in Medical Research Literature | https://aclanthology.org/2021.emnlp-main.486/ | [
"Yingya Li",
"Jun Wang",
"Bei Yu"
] | Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. However, no current health information system supports the direct retrieval of health advice. This study fills the gap by developing and validating an NLP-based prediction model for identify... | 2021.emnlp-main.486 | 10.18653/v1/2021.emnlp-main.486 | null | null | null |
2021.emnlp-main.487 | A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading | https://aclanthology.org/2021.emnlp-main.487/ | [
"Zhaohui Li",
"Yajur Tomar",
"Rebecca J. Passonneau"
] | Automatic short answer grading (ASAG) is the task of assessing students’ short natural language responses to objective questions. It is a crucial component of new education platforms, and could support more wide-spread use of constructed response questions to replace cognitively less challenging multiple choice questio... | 2021.emnlp-main.487 | 10.18653/v1/2021.emnlp-main.487 | null | null | null |
2021.emnlp-main.488 | Evaluating Scholarly Impact: Towards Content-Aware Bibliometrics | https://aclanthology.org/2021.emnlp-main.488/ | [
"Saurav Manchanda",
"George Karypis"
] | Quantitatively measuring the impact-related aspects of scientific, engineering, and technological (SET) innovations is a fundamental problem with broad applications. Traditional citation-based measures for assessing the impact of innovations and related entities do not take into account the content of the publications.... | 2021.emnlp-main.488 | 10.18653/v1/2021.emnlp-main.488 | null | null | null |
2021.emnlp-main.489 | A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems | https://aclanthology.org/2021.emnlp-main.489/ | [
"Sunghyun Park",
"Han Li",
"Ameen Patel",
"Sidharth Mudgal",
"Sungjin Lee",
"Young-Bum Kim",
"Spyros Matsoukas",
"Ruhi Sarikaya"
] | Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request. We propose a scalable and automatic approach for improving NLU in a large-scale conversational AI system by leveraging im... | 2021.emnlp-main.489 | 10.18653/v1/2021.emnlp-main.489 | null | 2010.12251 | title_snapshot |
2021.emnlp-main.490 | Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension | https://aclanthology.org/2021.emnlp-main.490/ | [
"Naoya Inoue",
"Harsh Trivedi",
"Steven Sinha",
"Niranjan Balasubramanian",
"Kentaro Inui"
] | How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient fo... | 2021.emnlp-main.490 | 10.18653/v1/2021.emnlp-main.490 | null | 2109.06853 | title_snapshot |
2021.emnlp-main.491 | FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models | https://aclanthology.org/2021.emnlp-main.491/ | [
"Rakesh Chada",
"Pradeep Natarajan"
] | The task of learning from only a few examples (called a few-shot setting) is of key importance and relevance to a real-world setting. For question answering (QA), the current state-of-the-art pre-trained models typically need fine-tuning on tens of thousands of examples to obtain good results. Their performance degrade... | 2021.emnlp-main.491 | 10.18653/v1/2021.emnlp-main.491 | null | 2109.01951 | title_snapshot |
2021.emnlp-main.492 | Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval | https://aclanthology.org/2021.emnlp-main.492/ | [
"Jing Lu",
"Gustavo Hernandez Abrego",
"Ji Ma",
"Jianmo Ni",
"Yinfei Yang"
] | In the context of neural passage retrieval, we study three promising techniques: synthetic data generation, negative sampling, and fusion. We systematically investigate how these techniques contribute to the performance of the retrieval system and how they complement each other. We propose a multi-stage framework compr... | 2021.emnlp-main.492 | 10.18653/v1/2021.emnlp-main.492 | null | 2010.12523 | title_judge |
2021.emnlp-main.493 | Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA | https://aclanthology.org/2021.emnlp-main.493/ | [
"Manuel R. Ciosici",
"Joe Cecil",
"Alex Hedges",
"Dong-Ho Lee",
"Marjorie Freedman",
"Ralph Weischedel"
] | Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. PTLMs have shown great success in many question-answering tasks, given significant... | 2021.emnlp-main.493 | 10.18653/v1/2021.emnlp-main.493 | null | 2110.01552 | title_snapshot |
2021.emnlp-main.494 | ReasonBERT: Pre-trained to Reason with Distant Supervision | https://aclanthology.org/2021.emnlp-main.494/ | [
"Xiang Deng",
"Yu Su",
"Alyssa Lees",
"You Wu",
"Cong Yu",
"Huan Sun"
] | We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. Unlike existing pre-training methods that only harvest learning signals from local contexts of naturally occurring texts, we propose a generalized notio... | 2021.emnlp-main.494 | 10.18653/v1/2021.emnlp-main.494 | null | 2109.04912 | title_snapshot |
2021.emnlp-main.495 | Single-dataset Experts for Multi-dataset Question Answering | https://aclanthology.org/2021.emnlp-main.495/ | [
"Dan Friedman",
"Ben Dodge",
"Danqi Chen"
] | Many datasets have been created for training reading comprehension models, and a natural question is whether we can combine them to build models that (1) perform better on all of the training datasets and (2) generalize and transfer better to new datasets. Prior work has addressed this goal by training one network simu... | 2021.emnlp-main.495 | 10.18653/v1/2021.emnlp-main.495 | null | 2109.13880 | title_snapshot |
2021.emnlp-main.496 | Simple Entity-Centric Questions Challenge Dense Retrievers | https://aclanthology.org/2021.emnlp-main.496/ | [
"Christopher Sciavolino",
"Zexuan Zhong",
"Jinhyuk Lee",
"Danqi Chen"
] | Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised training examples. However, in this paper, we demonstrate current dense models are not yet the holy grail of retrieval. We first construct Entit... | 2021.emnlp-main.496 | 10.18653/v1/2021.emnlp-main.496 | null | 2109.08535 | title_snapshot |
2021.emnlp-main.497 | Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization | https://aclanthology.org/2021.emnlp-main.497/ | [
"Ansong Ni",
"Matt Gardner",
"Pradeep Dasigi"
] | Question Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information for reasoning. The retrieval model is typically trained to maximize the likelihood of the labeled supporting evidence. However, when retrieving from large text corpora such as Wik... | 2021.emnlp-main.497 | 10.18653/v1/2021.emnlp-main.497 | null | 2103.12235 | title_snapshot |
2021.emnlp-main.498 | MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents | https://aclanthology.org/2021.emnlp-main.498/ | [
"Song Feng",
"Siva Sankalp Patel",
"Hui Wan",
"Sachindra Joshi"
] | We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios w... | 2021.emnlp-main.498 | 10.18653/v1/2021.emnlp-main.498 | null | 2109.12595 | title_snapshot |
2021.emnlp-main.499 | GupShup: Summarizing Open-Domain Code-Switched Conversations | https://aclanthology.org/2021.emnlp-main.499/ | [
"Laiba Mehnaz",
"Debanjan Mahata",
"Rakesh Gosangi",
"Uma Sushmitha Gunturi",
"Riya Jain",
"Gauri Gupta",
"Amardeep Kumar",
"Isabelle G. Lee",
"Anish Acharya",
"Rajiv Ratn Shah"
] | Code-switching is the communication phenomenon where the speakers switch between different languages during a conversation. With the widespread adoption of conversational agents and chat platforms, code-switching has become an integral part of written conversations in many multi-lingual communities worldwide. Therefore... | 2021.emnlp-main.499 | 10.18653/v1/2021.emnlp-main.499 | null | 2104.08578 | title_judge |
2021.emnlp-main.500 | BiSECT: Learning to Split and Rephrase Sentences with Bitexts | https://aclanthology.org/2021.emnlp-main.500/ | [
"Joongwon Kim",
"Mounica Maddela",
"Reno Kriz",
"Wei Xu",
"Chris Callison-Burch"
] | An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this ‘split and rephrase’ task. Our BiSECT training data consists of 1 million long English... | 2021.emnlp-main.500 | 10.18653/v1/2021.emnlp-main.500 | null | 2109.05006 | title_snapshot |
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