Abstract
Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity/relation linking results.- Anthology ID:
- D19-1263
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2614–2622
- Language:
- URL:
- https://aclanthology.org/D19-1263
- DOI:
- 10.18653/v1/D19-1263
- Cite (ACL):
- Jiwei Ding, Wei Hu, Qixin Xu, and Yuzhong Qu. 2019. Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2614–2622, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering (Ding et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D19-1263.pdf
- Data
- WebQuestions