Abstract
Aligning a sequence of words to one of its infrequent translations is a difficult task. We propose a simple and original solution to this problem that yields to significant gains over a state-of-the-art transpotting task. Our approach consists in aligning non parallel sentences from the training data in order to reinforce online the alignment models. We show that using only a few pairs of non parallel sentences allows to improve significantly the alignment of infrequent translations.- Anthology ID:
- 2012.amta-papers.2
- Volume:
- Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
- Month:
- October 28-November 1
- Year:
- 2012
- Address:
- San Diego, California, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2012.amta-papers.2
- DOI:
- Cite (ACL):
- Julien Bourdaillet and Philippe Langlais. 2012. Identifying Infrequent Translations by Aligning Non Parallel Sentences. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Identifying Infrequent Translations by Aligning Non Parallel Sentences (Bourdaillet & Langlais, AMTA 2012)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2012.amta-papers.2.pdf