A Discriminative Latent-Variable Model for Bilingual Lexicon Induction
Sebastian Ruder, Ryan Cotterell, Yova Kementchedjhieva, Anders Søgaard
Abstract
We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical improvements on six language pairs under two metrics and show that the prior theoretically and empirically helps to mitigate the hubness problem. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior.- Anthology ID:
- D18-1042
- 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:
- 458–468
- Language:
- URL:
- https://aclanthology.org/D18-1042
- DOI:
- 10.18653/v1/D18-1042
- Cite (ACL):
- Sebastian Ruder, Ryan Cotterell, Yova Kementchedjhieva, and Anders Søgaard. 2018. A Discriminative Latent-Variable Model for Bilingual Lexicon Induction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 458–468, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- A Discriminative Latent-Variable Model for Bilingual Lexicon Induction (Ruder et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/D18-1042.pdf
- Code
- sebastianruder/latent-variable-vecmap