Lingxi Zhang
2023
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering
Lingxi Zhang
|
Jing Zhang
|
Yanling Wang
|
Shulin Cao
|
Xinmei Huang
|
Cuiping Li
|
Hong Chen
|
Juanzi Li
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
The generalization problem on KBQA has drawn considerable attention. Existing research suffers from the generalization issue brought by the entanglement in the coarse-grained modeling of the logical expression, or inexecutability issues due to the fine-grained modeling of disconnected classes and relations in real KBs. We propose a Fine-to-Coarse Composition framework for KBQA (FC-KBQA) to both ensure the generalization ability and executability of the logical expression. The main idea of FC-KBQA is to extract relevant fine-grained knowledge components from KB and reformulate them into middle-grained knowledge pairs for generating the final logical expressions. FC-KBQA derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than the baseline. Our code is now available at GitHub https://github.com/RUCKBReasoning/FC-KBQA.
Search
Co-authors
- Jing Zhang 1
- Yanling Wang 1
- Shulin Cao 1
- Xinmei Huang 1
- Cuiping Li 1
- show all...
Venues
- acl1