1.3k citations · 8.8k across the 127 of their papers we have counts for
18 papers · 2 filters
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi +2
Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationshi…
MKD: a Multi-Task Knowledge Distillation Approach for Pretrained Language Models
Linqing Liu, Huan Wang, Jimmy Lin +2
Pretrained language models have led to significant performance gains in many NLP tasks. However, the intensive computing resources to train such models remain an issue. Knowledge d…
ERASER: A Benchmark to Evaluate Rationalized NLP Models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani +4
State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased inte…
Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework
Michael Shum, Stephan Zheng, Wojciech Kryściński +2
Human-like chit-chat conversation requires agents to generate responses that are fluent, engaging and consistent. We propose Sketch-Fill-A-R, a framework that uses a persona-memory…
Evaluating the Factual Consistency of Abstractive Text Summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong +1
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, m…
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Jian-Guo Zhang, Kazuma Hashimoto, Chien-Sheng Wu +4
Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST mainly fall into one of two categories, namely, ontology-based and onto…
CTRL: A Conditional Transformer Language Model for Controllable Generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney +2
Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text. We release CTRL, a 1.63 billion-p…
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Tao Yu, Rui Zhang, He Yang Er +21
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained f…
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
Rui Zhang, Tao Yu, He Yang Er +7
We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and…
Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression
Tong Niu, Caiming Xiong, Richard Socher
Text compression has diverse applications such as Summarization, Reading Comprehension and Text Editing. However, almost all existing approaches require either hand-crafted feature…
Neural Text Summarization: A Critical Evaluation
Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann +2
Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document. Despite increased interest in the communit…
Explain Yourself! Leveraging Language Models for Commonsense Reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong +1
Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately…
SParC: Cross-Domain Semantic Parsing in Context
Tao Yu, Rui Zhang, Michihiro Yasunaga +16
We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It…
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering
Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar +2
While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource lan…
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl +3
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking. Existing approaches gene…
Unifying Question Answering, Text Classification, and Regression via Span Extraction
Nitish Shirish Keskar, Bryan McCann, Caiming Xiong +1
Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significant…
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
Chien-Sheng Wu, Richard Socher, Caiming Xiong
End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local…
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
Victor Zhong, Caiming Xiong, Nitish Shirish Keskar +1
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear c…