An Imitation Game for Learning Semantic Parsers from User Interaction

Ziyu Yao, Yiqi Tang, Wen-tau Yih, Huan Sun, Yu Su


Abstract
Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users. A semantic parser should be introspective of its uncertainties and prompt for user demonstrations when uncertain. In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions. To combat the sparsity of demonstrations, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and retrains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011). We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem. Code will be available at https://github.com/sunlab-osu/MISP.
Anthology ID:
2020.emnlp-main.559
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6883–6902
Language:
URL:
https://aclanthology.org/2020.emnlp-main.559
DOI:
10.18653/v1/2020.emnlp-main.559
Bibkey:
Cite (ACL):
Ziyu Yao, Yiqi Tang, Wen-tau Yih, Huan Sun, and Yu Su. 2020. An Imitation Game for Learning Semantic Parsers from User Interaction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6883–6902, Online. Association for Computational Linguistics.
Cite (Informal):
An Imitation Game for Learning Semantic Parsers from User Interaction (Yao et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.559.pdf
Video:
 https://slideslive.com/38938921
Code
 sunlab-osu/MISP
Data
SPIDERWikiSQL