Abstract
Multilingual machine translation addresses the task of translating between multiple source and target languages. We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence neural multilingual translation. Our approach seeks to retain as much of the parameter sharing generalization of NMT models as possible, while still allowing for language-specific specialization of the attention model to a particular language-pair or task. Our experiments on four languages of the Europarl corpus show that using a target-specific model of attention provides consistent gains in translation quality for all possible translation directions, compared to a model in which all parameters are shared. We observe improved translation quality even in the (extreme) low-resource zero-shot translation directions for which the model never saw explicitly paired parallel data.- Anthology ID:
- C18-1263
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3112–3122
- Language:
- URL:
- https://aclanthology.org/C18-1263
- DOI:
- Cite (ACL):
- Graeme Blackwood, Miguel Ballesteros, and Todd Ward. 2018. Multilingual Neural Machine Translation with Task-Specific Attention. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3112–3122, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Multilingual Neural Machine Translation with Task-Specific Attention (Blackwood et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/C18-1263.pdf