Look-up and Adapt: A One-shot Semantic Parser

Zhichu Lu, Forough Arabshahi, Igor Labutov, Tom Mitchell

Abstract
Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited “supported” domain of discourse and fail drastically when faced with an out-of-domain utterance, mainly due to the limitations of their semantic parser. In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch. Our parser maintains a memory consisting of a representative subset of the seen utterances paired with their logical forms. Given an unseen utterance, our parser works by looking up a similar utterance from the memory and adapting its logical form until it fits the unseen utterance. Moreover, we present a data generation strategy for constructing utterance-logical form pairs from different domains. Our results show an improvement of up to 68.8% on one-shot parsing under two different evaluation settings compared to the baselines.
Anthology ID:
D19-1104
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1129–1139
Language:
URL:
https://aclanthology.org/D19-1104
DOI:
10.18653/v1/D19-1104
Bibkey:
Cite (ACL):
Zhichu Lu, Forough Arabshahi, Igor Labutov, and Tom Mitchell. 2019. Look-up and Adapt: A One-shot Semantic Parser. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1129–1139, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Look-up and Adapt: A One-shot Semantic Parser (Lu et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/D19-1104.pdf
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 D19-1104.Attachment.zip
Code
 zhichul/lookup-and-adapt-parser-data
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