@inproceedings{upadhyay-etal-2018-robust,
title = "Robust Cross-Lingual Hypernymy Detection Using Dependency Context",
author = "Upadhyay, Shyam and
Vyas, Yogarshi and
Carpuat, Marine and
Roth, Dan",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1056",
doi = "10.18653/v1/N18-1056",
pages = "607--618",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Robust Cross-Lingual Hypernymy Detection Using Dependency Context
%A Upadhyay, Shyam
%A Vyas, Yogarshi
%A Carpuat, Marine
%A Roth, Dan
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F upadhyay-etal-2018-robust
%X 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.
%R 10.18653/v1/N18-1056
%U https://aclanthology.org/N18-1056
%U https://doi.org/10.18653/v1/N18-1056
%P 607-618
Markdown (Informal)
[Robust Cross-Lingual Hypernymy Detection Using Dependency Context](https://aclanthology.org/N18-1056) (Upadhyay et al., NAACL 2018)
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.