Robust Cross-lingual Hypernymy Detection using Dependency Context
This work addresses cross-lingual hypernymy detection, aiding tasks like textual entailment and event coreference, with incremental improvements in method robustness and dataset creation.
The paper tackles cross-lingual hypernymy detection by proposing BISPARSE-DEP, an unsupervised method that learns sparse bilingual word embeddings from dependency contexts, significantly improving performance over lexical context-based approaches and demonstrating robustness in low-resource settings with minimal performance loss.
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.