Abstract
Cross-lingual or cross-domain correspondences play key roles in tasks ranging from machine translation to transfer learning. Recently, purely unsupervised methods operating on monolingual embeddings have become effective alignment tools. Current state-of-the-art methods, however, involve multiple steps, including heuristic post-hoc refinement strategies. In this paper, we cast the correspondence problem directly as an optimal transport (OT) problem, building on the idea that word embeddings arise from metric recovery algorithms. Indeed, we exploit the Gromov-Wasserstein distance that measures how similarities between pairs of words relate across languages. We show that our OT objective can be estimated efficiently, requires little or no tuning, and results in performance comparable with the state-of-the-art in various unsupervised word translation tasks.- Anthology ID:
- D18-1214
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1881–1890
- Language:
- URL:
- https://aclanthology.org/D18-1214
- DOI:
- 10.18653/v1/D18-1214
- Cite (ACL):
- David Alvarez-Melis and Tommi Jaakkola. 2018. Gromov-Wasserstein Alignment of Word Embedding Spaces. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1881–1890, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Gromov-Wasserstein Alignment of Word Embedding Spaces (Alvarez-Melis & Jaakkola, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D18-1214.pdf