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Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback
- Source :
- ACL
- Publication Year :
- 2020
-
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.<br />ACL 2020
- Subjects :
- FOS: Computer and information sciences
SQL
Parsing
Interpretation (logic)
Computer Science - Computation and Language
Computer science
business.industry
02 engineering and technology
Construct (python library)
010501 environmental sciences
computer.software_genre
01 natural sciences
0202 electrical engineering, electronic engineering, information engineering
Logical form
020201 artificial intelligence & image processing
Artificial intelligence
business
Computation and Language (cs.CL)
computer
Utterance
Natural language
Natural language processing
0105 earth and related environmental sciences
computer.programming_language
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ACL
- Accession number :
- edsair.doi.dedup.....648d93a647a403c97bbca74ce4fd2f9d