Abstract
We study the task of semantic parse correction with natural language feedback. Given a natural language utterance, most semantic parsing systems pose the problem as one-shot translation where the utterance is mapped to a corresponding logical form. In this paper, we investigate a more interactive scenario where humans can further interact with the system by providing free-form natural language feedback to correct the system when it generates an inaccurate interpretation of an initial utterance. We focus on natural language to SQL systems and construct, SPLASH, a dataset of utterances, incorrect SQL interpretations and the corresponding natural language feedback. We compare various reference models for the correction task and show that incorporating such a rich form of feedback can significantly improve the overall semantic parsing accuracy while retaining the flexibility of natural language interaction. While we estimated human correction accuracy is 81.5%, our best model achieves only 25.1%, which leaves a large gap for improvement in future research. SPLASH is publicly available at https://aka.ms/Splash_dataset.- Anthology ID:
- 2020.acl-main.187
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2065–2077
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.187
- DOI:
- 10.18653/v1/2020.acl-main.187
- Cite (ACL):
- Ahmed Elgohary, Saghar Hosseini, and Ahmed Hassan Awadallah. 2020. Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2065–2077, Online. Association for Computational Linguistics.
- Cite (Informal):
- Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback (Elgohary et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.acl-main.187.pdf
- Data
- SPLASH, CoSQL, WikiSQL