Robust Cross-lingual Hypernymy Detection Using Dependency Contex

ACL 2018

Robust Cross-lingual Hypernymy Detection Using Dependency Contex

Jan 21, 2021
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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. Authors: Shyam Upadhyay, Yogarshi Vyas, Marine Carpuat, Dan Roth (University of Pennsylvania, University of Maryland)

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