Abstract
Complex question answering over knowledge base remains as a challenging task because it involves reasoning over multiple pieces of information, including intermediate entities/relations and other constraints. Previous methods simplify the SPARQL query of a question into such forms as a list or a graph, missing such constraints as “filter” and “order_by”, and present models specialized for generating those simplified forms from a given question. We instead introduce a novel approach that directly generates an executable SPARQL query without simplification, addressing the issue of generating unseen entities. We adapt large scale pre-trained encoder-decoder models and show that our method significantly outperforms the previous methods and also that our method has higher interpretability and computational efficiency than the previous methods.- Anthology ID:
- 2021.findings-emnlp.50
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 547–557
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.50
- DOI:
- 10.18653/v1/2021.findings-emnlp.50
- Cite (ACL):
- Xin Huang, Jung-Jae Kim, and Bowei Zou. 2021. Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 547–557, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation (Huang et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.findings-emnlp.50.pdf
- Data
- ComplexWebQuestions, MetaQA