Abstract
We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture composed of recurrent long short-term memory and deep feed forward networks. The results show that word- and character-level representations each improve state-of-the-art results for BLI, and the best results are obtained by exploiting the synergy between these word- and character-level representations in the classification model.- Anthology ID:
- E17-1102
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1085–1095
- Language:
- URL:
- https://aclanthology.org/E17-1102
- DOI:
- Cite (ACL):
- Geert Heyman, Ivan Vulić, and Marie-Francine Moens. 2017. Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1085–1095, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations (Heyman et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/E17-1102.pdf