43 citations · 163 across the 10 of their papers we have counts for
15 papers
TableFormer: Robust Transformer Modeling for Table-Text Encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay +3
Understanding tables is an important aspect of natural language understanding. Existing models for table understanding require linearization of the table structure, where row or co…
Graph-Based Decoding for Task Oriented Semantic Parsing
Jeremy R. Cole, Nanjiang Jiang, Panupong Pasupat +2
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In…
TIMEDIAL: Temporal Commonsense Reasoning in Dialog
Lianhui Qin, Aditya Gupta, Shyam Upadhyay +3
Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progr…
Few-shot Intent Classification and Slot Filling with Retrieved Examples
Dian Yu, Luheng He, Yuan Zhang +3
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce…
Neural Data Augmentation via Example Extrapolation
Kenton Lee, Kelvin Guu, Luheng He +2
In many applications of machine learning, certain categories of examples may be underrepresented in the training data, causing systems to underperform on such "few-shot" cases at t…
Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements
Yang Li, Gang Li, Luheng He +3
Natural language descriptions of user interface (UI) elements such as alternative text are crucial for accessibility and language-based interaction in general. Yet, these descripti…
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension
Daniel Andor, Luheng He, Kenton Lee +1
Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers. We e…
A General Framework for Information Extraction using Dynamic Span Graphs
Yi Luan, Dave Wadden, Luheng He +3
We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are constructed by s…
PAWS: Paraphrase Adversaries from Word Scrambling
Yuan Zhang, Jason Baldridge, Luheng He
Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like…
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Yi Luan, Luheng He, Mari Ostendorf +1
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annot…
Large-Scale QA-SRL Parsing
Nicholas FitzGerald, Julian Michael, Luheng He +1
We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, co…
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
Luheng He, Kenton Lee, Omer Levy +1
Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features.…
Higher-order Coreference Resolution with Coarse-to-fine Inference
Kenton Lee, Luheng He, Luke Zettlemoyer
We introduce a fully differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architectu…
Crowdsourcing Question-Answer Meaning Representations
Julian Michael, Gabriel Stanovsky, Luheng He +2
We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a cr…
End-to-end Neural Coreference Resolution
Kenton Lee, Luheng He, Mike Lewis +1
We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered ment…