most citedDC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

10 citations · 38 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2020

Towards Interpretable Reasoning over Paragraph Effects in Situation

Mucheng Ren, Xiubo Geng, Tao Qin +2

We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect described in a background paragraph, and apply the…

cs.CL2020

No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension

Xuguang Wang, Linjun Shou, Ming Gong +2

The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but a…

cs.CL20204 cited

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

Huaishao Luo, Lei Ji, Tianrui Li +2

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks…

cs.CL20203 cited

Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation

Chujie Zheng, Yunbo Cao, Daxin Jiang +1

In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been…

cs.CL202010 cited

Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues

Ruijian Xu, Chongyang Tao, Daxin Jiang +3

Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is a great challenging task. Existing studies focus on buildi…

cs.CL202010 cited

DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

Yuyu Zhang, Ping Nie, Xiubo Geng +3

Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT. State-of-the-art approaches typical…