@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://preview.aclanthology.org/ingest_wac_2008/N18-1056/",
    doi = "10.18653/v1/N18-1056",
    pages = "607--618",
    abstract = "Cross-lingual Hypernymy Detection involves determining if a word in one language ({\textquotedblleft}fruit{\textquotedblright}) is a hypernym of a word in another language ({\textquotedblleft}pomme{\textquotedblright} 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."
}