Learning to Learn Semantic Parsers from Natural Language Supervision

Igor Labutov, Bishan Yang, Tom Mitchell


Abstract
As humans, we often rely on language to learn language. For example, when corrected in a conversation, we may learn from that correction, over time improving our language fluency. Inspired by this observation, we propose a learning algorithm for training semantic parsers from supervision (feedback) expressed in natural language. Our algorithm learns a semantic parser from users’ corrections such as “no, what I really meant was before his job, not after”, by also simultaneously learning to parse this natural language feedback in order to leverage it as a form of supervision. Unlike supervision with gold-standard logical forms, our method does not require the user to be familiar with the underlying logical formalism, and unlike supervision from denotation, it does not require the user to know the correct answer to their query. This makes our learning algorithm naturally scalable in settings where existing conversational logs are available and can be leveraged as training data. We construct a novel dataset of natural language feedback in a conversational setting, and show that our method is effective at learning a semantic parser from such natural language supervision.
Anthology ID:
D18-1195
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1676–1690
Language:
URL:
https://aclanthology.org/D18-1195
DOI:
10.18653/v1/D18-1195
Bibkey:
Cite (ACL):
Igor Labutov, Bishan Yang, and Tom Mitchell. 2018. Learning to Learn Semantic Parsers from Natural Language Supervision. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1676–1690, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Learning to Learn Semantic Parsers from Natural Language Supervision (Labutov et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1195.pdf