Learning Joint Semantic Parsers from Disjoint Data

Hao Peng, Sam Thomson, Swabha Swayamdipta, Noah A. Smith


Abstract
We present a new approach to learning a semantic parser from multiple datasets, even when the target semantic formalisms are drastically different and the underlying corpora do not overlap. We handle such “disjoint” data by treating annotations for unobserved formalisms as latent structured variables. Building on state-of-the-art baselines, we show improvements both in frame-semantic parsing and semantic dependency parsing by modeling them jointly.
Anthology ID:
N18-1135
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1492–1502
Language:
URL:
https://www.aclweb.org/anthology/N18-1135
DOI:
10.18653/v1/N18-1135
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1135.pdf
Video:
 http://vimeo.com/277672994