@InProceedings{upadhyay-EtAl:2018:N18-1,
  author    = {Upadhyay, Shyam  and  Vyas, Yogarshi  and  Carpuat, Marine  and  Roth, Dan},
  title     = {Robust Cross-Lingual Hypernymy Detection Using Dependency Context},
  booktitle = {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},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-1056}
}