Zero-Shot Classification by Logical Reasoning on Natural Language Explanations

Chi Han, Hengzhi Pei, Xinya Du, Heng Ji


Abstract
Humans can classify data of an unseen category by reasoning on its language explanations. This ability is owing to the compositional nature of language: we can combine previously seen attributes to describe the new category. For example, we might describe a sage thrasher as “it has a slim straight relatively short bill, yellow eyes and a long tail”, so that others can use their knowledge of attributes “slim straight relatively short bill”, “yellow eyes” and “long tail” to recognize a sage thrasher. Inspired by this observation, in this work we tackle zero-shot classification task by logically parsing and reasoning on natural language explanations. To this end, we propose the framework CLORE (Classification by LOgical Reasoning on Explanations). While previous methods usually regard textual information as implicit features, CLORE parses explanations into logical structures and then explicitly reasons along this structure on the input to produce a classification score. Experimental results on explanation-based zero-shot classification benchmarks demonstrate that CLORE is superior to baselines, which we show is mainly due to higher scores on tasks requiring more logical reasoning. We also demonstrate that our framework can be extended to zero-shot classification on visual modality. Alongside classification decisions, CLORE can provide the logical parsing and reasoning process as a clear form of rationale. Through empirical analysis we demonstrate that CLORE is also less affected by linguistic biases than baselines.
Anthology ID:
2023.findings-acl.571
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8967–8981
Language:
URL:
https://aclanthology.org/2023.findings-acl.571
DOI:
10.18653/v1/2023.findings-acl.571
Bibkey:
Cite (ACL):
Chi Han, Hengzhi Pei, Xinya Du, and Heng Ji. 2023. Zero-Shot Classification by Logical Reasoning on Natural Language Explanations. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8967–8981, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Zero-Shot Classification by Logical Reasoning on Natural Language Explanations (Han et al., Findings 2023)
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PDF:
https://aclanthology.org/2023.findings-acl.571.pdf