Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation

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Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation
Title:
Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation
Other Titles:
Findings of the Association for Computational Linguistics: EMNLP 2021
Publication Date:
28 December 2021
Citation:
Huang, X., Kim, J.-J., & Zou, B. (2021). Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation. Findings of the Association for Computational Linguistics: EMNLP 2021. doi:10.18653/v1/2021.findings-emnlp.50
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic
Grant Reference no. : A18A2b0046
Description:
ISBN:
10.18653/v1/2021.findings-emnlp.50
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