Robust Cross-Lingual Hypernymy Detection Using Dependency Context

Shyam Upadhyay, Yogarshi Vyas, Marine Carpuat, Dan Roth


Abstract
Cross-lingual Hypernymy Detection involves determining if a word in one language (“fruit”) is a hypernym of a word in another language (“pomme” i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BiSparse-Dep, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BiSparse-Dep can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages – Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available.
Anthology ID:
N18-1056
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:
607–618
Language:
URL:
https://www.aclweb.org/anthology/N18-1056
DOI:
10.18653/v1/N18-1056
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1056.pdf
Video:
 http://vimeo.com/276412103