activity
20182021
most citedBenchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation

6 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20214 cited

Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision

Ning Bian, Xianpei Han, Bo Chen +3

Despite recent success in machine reading comprehension (MRC), learning high-quality MRC models still requires large-scale labeled training data, even using strong pre-trained lang…

cs.CL2021

From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding

Shan Wu, Bo Chen, Chunlei Xin +6

Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing metho…

cs.CL20216 cited

Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation

Ning Bian, Xianpei Han, Bo Chen +1

A fundamental ability of humans is to utilize commonsense knowledge in language understanding and question answering. In recent years, many knowledge-enhanced Commonsense Question…

cs.CL20191 cited

Sentence Rewriting for Semantic Parsing

Bo Chen, Le Sun, Xianpei Han +1

A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology. In this paper, we propose a sentence rewriting based semantic…

cs.CL2018

Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

Bo Chen, Le Sun, Xianpei Han

This paper proposes a neural semantic parsing approach -- Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultane…