Nowadays, question answering (QA) over complex questions is the most spotlighted research topic in diverse communities. Different from existing QA approaches for simple questions that require a single relation in a knowledge graph (KG) to find an answer set, QA for complex questions requires to process multiple KG relations. Complex questions often include some representations for constraints (ordinal, temporal, etc.) that restrict an answer set for the given question. Despite lots of efforts to process such questions, there are still limitations in processing constraints, multiple relations, and variables. To solve the issues, we propose a novel QA method that first decomposes an input question and then generates a correct query graph with fully complete semantics. In order to fill with lossy semantics caused by the decomposition task, the proposed method employs Bi-GRU based model. The model integrates individual compositional semantics of sub-query graphs matched to decomposed sub-questions. Experimental results show the best performance compared to the state-of-art on complex questions.
Bibliographical noteFunding Information:
This research was supported by the Graduate School of YONSEI University Research Scholarship Grants in 2019 and the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIP; Ministry of Science, ICT & Future Planning) (No. NRF-2019R1A2B5B01070555 ).
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence