Multilingual Semantic Parsing And Code-Switching

Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen, Mark Johnson

Abstract
Extending semantic parsing systems to new domains and languages is a highly expensive, time-consuming process, so making effective use of existing resources is critical. In this paper, we describe a transfer learning method using crosslingual word embeddings in a sequence-to-sequence model. On the NLmaps corpus, our approach achieves state-of-the-art accuracy of 85.7% for English. Most importantly, we observed a consistent improvement for German compared with several baseline domain adaptation techniques. As a by-product of this approach, our models that are trained on a combination of English and German utterances perform reasonably well on code-switching utterances which contain a mixture of English and German, even though the training data does not contain any such. As far as we know, this is the first study of code-switching in semantic parsing. We manually constructed the set of code-switching test utterances for the NLmaps corpus and achieve 78.3% accuracy on this dataset.
Anthology ID:
K17-1038
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
379–389
Language:
URL:
https://aclanthology.org/K17-1038
DOI:
10.18653/v1/K17-1038
Bibkey:
Cite (ACL):
Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen, and Mark Johnson. 2017. Multilingual Semantic Parsing And Code-Switching. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 379–389, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Multilingual Semantic Parsing And Code-Switching (Duong et al., CoNLL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/K17-1038.pdf
Code
 vbtagitlab/code-switching
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