| Paper ID | HLT-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 |
| Session | HLT-17: Language Understanding 5: Question Answering and Reading Comprehension |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
