Abstract
Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. We propose a bidirectional recurrent neural network based approach to extract parallel sentences from collections of multilingual texts. Our experiments with noisy parallel corpora show that we can achieve promising results against a competitive baseline by removing the need of specific feature engineering or additional external resources. To justify the utility of our approach, we extract sentence pairs from Wikipedia articles to train machine translation systems and show significant improvements in translation performance.- Anthology ID:
- C18-1122
- 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:
- 1442–1453
- Language:
- URL:
- https://aclanthology.org/C18-1122
- DOI:
- Cite (ACL):
- Francis Grégoire and Philippe Langlais. 2018. Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1442–1453, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation (Grégoire & Langlais, COLING 2018)
- PDF:
- https://preview.aclanthology.org/landing_page/C18-1122.pdf
- Code
- FrancisGregoire/parSentExtract
- Data
- Europarl