Identifying Domain Adjacent Instances for Semantic Parsers

James Ferguson, Janara Christensen, Edward Li, Edgar Gonzàlez


Abstract
When the semantics of a sentence are not representable in a semantic parser’s output schema, parsing will inevitably fail. Detection of these instances is commonly treated as an out-of-domain classification problem. However, there is also a more subtle scenario in which the test data is drawn from the same domain. In addition to formalizing this problem of domain-adjacency, we present a comparison of various baselines that could be used to solve it. We also propose a new simple sentence representation that emphasizes words which are unexpected. This approach improves the performance of a downstream semantic parser run on in-domain and domain-adjacent instances.
Anthology ID:
D18-1539
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:
4964–4969
Language:
URL:
https://aclanthology.org/D18-1539
DOI:
10.18653/v1/D18-1539
Bibkey:
Cite (ACL):
James Ferguson, Janara Christensen, Edward Li, and Edgar Gonzàlez. 2018. Identifying Domain Adjacent Instances for Semantic Parsers. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4964–4969, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Identifying Domain Adjacent Instances for Semantic Parsers (Ferguson et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1539.pdf