Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback

Ahmed Elgohary, Saghar Hosseini, Ahmed Hassan Awadallah

Abstract Paper Share

Dialogue and Interactive Systems Long Paper

Session 4A: Jul 6 (17:00-18:00 GMT)
Session 5A: Jul 6 (20:00-21:00 GMT)
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.
You can open the pre-recorded video in a separate window.
NOTE: The SlidesLive video may display a random order of the authors. The correct author list is shown at the top of this webpage.

Similar Papers

Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen
Yixin Cao, Ruihao Shui, Liangming Pan, Min-Yen Kan, Zhiyuan Liu, Tat-Seng Chua,
A representative figure from paper main.100
Few-Shot NLG with Pre-Trained Language Model
Zhiyu Chen, Harini Eavani, Wenhu Chen, Yinyin Liu, William Yang Wang,
A representative figure from paper main.18
Logical Natural Language Generation from Open-Domain Tables
Wenhu Chen, Jianshu Chen, Yu Su, Zhiyu Chen, William Yang Wang,
A representative figure from paper main.708
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, Matthew Richardson,
A representative figure from paper main.677