2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-17.2
Paper Title COARSE-TO-CAREFUL: SEEKING SEMANTIC-RELATED KNOWLEDGE FOR OPEN-DOMAIN COMMONSENSE QUESTION ANSWERING
Authors Luxi Xing, Yue Hu, Jing Yu, Yuqiang Xie, Wei Peng, Institute of Information Engineering, Chinese Academy of Sciences, China
SessionHLT-17: Language Understanding 5: Question Answering and Reading Comprehension
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Human Language Technology: [HLT-UNDE] Spoken Language Understanding and Computational Semantics
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract It is prevalent to utilize external knowledge to help machine answer questions that need background commonsense, which faces a problem that unlimited knowledge will transmit noisy and misleading information. Towards the issue of introducing related knowledge, we propose a semantic-driven knowledge- aware QA framework, which controls the knowledge injection in a coarse-to-careful fashion. We devise a tailoring strategy to filter extracted knowledge under monitoring of the coarse semantic of question on the knowledge extraction stage. And we develop a semantic-aware knowledge fetching module that engages structural knowledge information and fuses proper knowledge according to the careful semantic of question in a hierarchical way. Experiment results illustrate that the proposed approach promotes the performance on the CommonsenseQA dataset comparing with strong baselines.