NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction
Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah
Abstract
We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.- Anthology ID:
- 2021.naacl-main.444
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5599–5610
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.444
- DOI:
- 10.18653/v1/2021.naacl-main.444
- Cite (ACL):
- Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, and Ahmed Hassan Awadallah. 2021. NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5599–5610, Online. Association for Computational Linguistics.
- Cite (Informal):
- NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction (Elgohary et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.naacl-main.444.pdf
- Data
- SPLASH