Abstract
We present InstaMap, an instance-based method for learning projection-based cross-lingual word embeddings. Unlike prior work, it deviates from learning a single global linear projection. InstaMap is a non-parametric model that learns a non-linear projection by iteratively: (1) finding a globally optimal rotation of the source embedding space relying on the Kabsch algorithm, and then (2) moving each point along an instance-specific translation vector estimated from the translation vectors of the point’s nearest neighbours in the training dictionary. We report performance gains with InstaMap over four representative state-of-the-art projection-based models on bilingual lexicon induction across a set of 28 diverse language pairs. We note prominent improvements, especially for more distant language pairs (i.e., languages with non-isomorphic monolingual spaces).- Anthology ID:
- 2020.acl-main.675
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7548–7555
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.675
- DOI:
- 10.18653/v1/2020.acl-main.675
- Cite (ACL):
- Goran Glavaš and Ivan Vulić. 2020. Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7548–7555, Online. Association for Computational Linguistics.
- Cite (Informal):
- Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces (Glavaš & Vulić, ACL 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.acl-main.675.pdf