Abstract
Semantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation. Most approaches to this task have been evaluated on a small number of existing corpora which assume that all utterances must be interpreted according to a database and typically ignore context. In this paper we present a new, publicly available corpus for context-dependent semantic parsing. The MRL used for the annotation was designed to support a portable, interactive tourist information system. We develop a semantic parser for this corpus by adapting the imitation learning algorithm DAgger without requiring alignment information during training. DAgger improves upon independently trained classifiers by 9.0 and 4.8 points in F-score on the development and test sets respectively.- Anthology ID:
- Q14-1042
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 2
- Month:
- Year:
- 2014
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 547–560
- Language:
- URL:
- https://aclanthology.org/Q14-1042
- DOI:
- 10.1162/tacl_a_00202
- Cite (ACL):
- Andreas Vlachos and Stephen Clark. 2014. A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing. Transactions of the Association for Computational Linguistics, 2:547–560.
- Cite (Informal):
- A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing (Vlachos & Clark, TACL 2014)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/Q14-1042.pdf