Abstract
The most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist of multiple entities and relations. Previous work largely focused on selecting the correct semantic relations for a question and disregarded the structure of the semantic parse: the connections between entities and the directions of the relations. We propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. We show on two data sets that the graph networks outperform all baseline models that do not explicitly model the structure. The error analysis confirms that our approach can successfully process complex semantic parses.- Anthology ID:
- C18-1280
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3306–3317
- Language:
- URL:
- https://aclanthology.org/C18-1280
- DOI:
- Cite (ACL):
- Daniil Sorokin and Iryna Gurevych. 2018. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3306–3317, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering (Sorokin & Gurevych, COLING 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/C18-1280.pdf
- Data
- WebQuestions, WebQuestionsSP