@inproceedings{elgohary-etal-2020-speak,
    title = "Speak to your Parser: Interactive Text-to-{SQL} with Natural Language Feedback",
    author = "Elgohary, Ahmed  and
      Hosseini, Saghar  and
      Hassan Awadallah, Ahmed",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.acl-main.187/",
    doi = "10.18653/v1/2020.acl-main.187",
    pages = "2065--2077",
    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 \url{https://aka.ms/Splash_dataset}."
}