| Paper ID | HLT-17.4 |
| Paper Title |
INTEGRATING SUBGRAPH-AWARE RELATION AND DIRECTION REASONING FOR QUESTION ANSWERING |
| Authors |
Xu Wang, Shuai Zhao, Bo Cheng, Jiale Han, Yingting Li, Beijing University of Posts and Telecommunications, China; Hao Yang, Huawei, China; Ivan Sekulic, University of Lugano, Switzerland; Guoshun Nan, Singapore University of Technology and Design, Singapore |
| 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 |
Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most of these models solely rely on fixed relation representations to obtain answers for different question-related KB subgraphs. Hence, the rich structured information of these subgraphs may be overlooked by the relation representation vectors. Meanwhile, the direction information of reasoning, which has been proven effective for the answer prediction on graphs, has not been fully explored in existing work. To address these challenges, we propose a novel neural model, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information. Additionally, we utilize direction information to enhance the reasoning ability. Experimental results show that our model yields substantial improvements on two widely used datasets. |
