@inproceedings{xu-koehn-2021-zero,
    title = "Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation",
    author = "Xu, Haoran  and
      Koehn, Philipp",
    editor = "Ben-David, Eyal  and
      Cohen, Shay  and
      McDonald, Ryan  and
      Plank, Barbara  and
      Reichart, Roi  and
      Rotman, Guy  and
      Ziser, Yftah",
    booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.adaptnlp-1.21/",
    pages = "204--213",
    abstract = "Linear embedding transformation has been shown to be effective for zero-shot cross-lingual transfer tasks and achieve surprisingly promising results. However, cross-lingual embedding space mapping is usually studied in static word-level embeddings, where a space transformation is derived by aligning representations of translation pairs that are referred from dictionaries. We move further from this line and investigate a contextual embedding alignment approach which is sense-level and dictionary-free. To enhance the quality of the mapping, we also provide a deep view of properties of contextual embeddings, i.e., the anisotropy problem and its solution. Experiments on zero-shot dependency parsing through the concept-shared space built by our embedding transformation substantially outperform state-of-the-art methods using multilingual embeddings."
}