Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database

Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, Yingbo Zhou


Abstract
Parsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB). However, existing approaches on semantic parsing cannot adapt to both modalities, as they suffer from the exponential growth of the logical form candidates and can hardly generalize to unseen data. In this work, we propose Uni-Parser, a unified semantic parser for question answering (QA) on both KB and DB. We define the primitive (relation and entity in KB, and table name, column name and cell value in DB) as the essential element in our framework. The number of primitives grows only at a linear rate to the number of retrieved relations in KB and DB, preventing us from exponential logic form candidates. We leverage the generator to predict final logical forms by altering and composing top-ranked primitives with different operations (e.g. select, where, count). With sufficiently pruned search space by a contrastive primitive ranker, the generator is empowered to capture the composition of primitives enhancing its generalization ability. We achieve competitive results on multiple KB and DB QA benchmarks with more efficiency, especially in the compositional and zero-shot settings.
Anthology ID:
2022.emnlp-main.605
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8858–8869
Language:
URL:
https://aclanthology.org/2022.emnlp-main.605
DOI:
10.18653/v1/2022.emnlp-main.605
Bibkey:
Cite (ACL):
Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, and Yingbo Zhou. 2022. Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8858–8869, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database (Liu et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.605.pdf