Abstract
We extend discriminative n-gram language modeling techniques originally proposed for automatic speech recognition to a statistical machine translation task. In this context, we propose a novel data selection method that leads to good models using a fraction of the training data. We carry out systematic experiments on several benchmark tests for Chinese to English translation using a hierarchical phrase-based machine translation system, and show that a discriminative language model significantly improves upon a state-of-the-art baseline. The experiments also highlight the benefits of our data selection method.- Anthology ID:
- 2008.amta-papers.12
- Volume:
- Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers
- Month:
- October 21-25
- Year:
- 2008
- Address:
- Waikiki, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 133–142
- Language:
- URL:
- https://aclanthology.org/2008.amta-papers.12
- DOI:
- Cite (ACL):
- Zhifei Li and Sanjeev Khudanpur. 2008. Large-scale Discriminative n-gram Language Models for Statistical Machine Translation. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers, pages 133–142, Waikiki, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Large-scale Discriminative n-gram Language Models for Statistical Machine Translation (Li & Khudanpur, AMTA 2008)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2008.amta-papers.12.pdf