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
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
607–618
Language:
URL:
https://aclanthology.org/N18-1056
DOI:
10.18653/v1/N18-1056
Bibkey:
Cite (ACL):
Shyam Upadhyay, Yogarshi Vyas, Marine Carpuat, and Dan Roth. 2018. Robust Cross-Lingual Hypernymy Detection Using Dependency Context. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 607–618, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Robust Cross-Lingual Hypernymy Detection Using Dependency Context (Upadhyay et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1056.pdf
Video:
 https://aclanthology.org/N18-1056.mp4
Code
 yogarshi/bisparse-dep