Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation

Kevin Yang, Olivia Deng, Charles Chen, Richard Shin, Subhro Roy, Benjamin Van Durme


Abstract
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al. 2020), we observe 33% relative improvement over a non-data-augmented baseline in top-1 match.
Anthology ID:
2022.findings-acl.291
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3685–3695
Language:
URL:
https://aclanthology.org/2022.findings-acl.291
DOI:
10.18653/v1/2022.findings-acl.291
Bibkey:
Cite (ACL):
Kevin Yang, Olivia Deng, Charles Chen, Richard Shin, Subhro Roy, and Benjamin Van Durme. 2022. Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3685–3695, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation (Yang et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.291.pdf
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