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.- Anthology ID:
- 2021.adaptnlp-1.21
- Volume:
- Proceedings of the Second Workshop on Domain Adaptation for NLP
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv, Ukraine
- Editors:
- Eyal Ben-David, Shay Cohen, Ryan McDonald, Barbara Plank, Roi Reichart, Guy Rotman, Yftah Ziser
- Venue:
- AdaptNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 204–213
- Language:
- URL:
- https://aclanthology.org/2021.adaptnlp-1.21
- DOI:
- Cite (ACL):
- Haoran Xu and Philipp Koehn. 2021. Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation. In Proceedings of the Second Workshop on Domain Adaptation for NLP, pages 204–213, Kyiv, Ukraine. Association for Computational Linguistics.
- Cite (Informal):
- Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation (Xu & Koehn, AdaptNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.adaptnlp-1.21.pdf
- Code
- fe1ixxu/ZeroShot-CrossLing-Parsing