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Case-Based Reasoning for Natural Language Queries over Knowledge Bases
Cited 15 time in
Web of Science
Cited 63 time in Scopus
- Authors
- Issue Date
- 2021-11
- Publisher
- ASSOC COMPUTATIONAL LINGUISTICS-ACL
- Citation
- 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021), pp.9594-9611
- Abstract
- It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions - a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the COMPLEXWEBQUESTIONS dataset, CBR-KBQA outperforms the current state of the art by 11% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases without any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.
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