Abstract
Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure. It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings. In this work, we present ClassyMap, a classification-based approach to self-learning, yielding a more robust and a more effective induction of projection-based CLWEs. Unlike prior self-learning methods, our approach allows for integration of diverse features into the iterative process. We show the benefits of ClassyMap for bilingual lexicon induction: we report consistent improvements in a weakly supervised setup (500 seed translation pairs) on a benchmark with 28 language pairs.- Anthology ID:
- 2020.acl-main.618
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6915–6922
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.618
- DOI:
- 10.18653/v1/2020.acl-main.618
- Cite (ACL):
- Mladen Karan, Ivan Vulić, Anna Korhonen, and Goran Glavaš. 2020. Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6915–6922, Online. Association for Computational Linguistics.
- Cite (Informal):
- Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction (Karan et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.618.pdf