Structural support vector machines for log-linear approach in statistical machine translation
Katsuhiko Hayashi, Taro Watanabe, Hajime Tsukada, Hideki Isozaki
Abstract
Minimum error rate training (MERT) is a widely used learning method for statistical machine translation. In this paper, we present a SVM-based training method to enhance generalization ability. We extend MERT optimization by maximizing the margin between the reference and incorrect translations under the L2-norm prior to avoid overfitting problem. Translation accuracy obtained by our proposed methods is more stable in various conditions than that obtained by MERT. Our experimental results on the French-English WMT08 shared task show that degrade of our proposed methods is smaller than that of MERT in case of small training data or out-of-domain test data.- Anthology ID:
- 2009.iwslt-papers.3
- Volume:
- Proceedings of the 6th International Workshop on Spoken Language Translation: Papers
- Month:
- December 1-2
- Year:
- 2009
- Address:
- Tokyo, Japan
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Note:
- Pages:
- 144–151
- Language:
- URL:
- https://aclanthology.org/2009.iwslt-papers.3
- DOI:
- Cite (ACL):
- Katsuhiko Hayashi, Taro Watanabe, Hajime Tsukada, and Hideki Isozaki. 2009. Structural support vector machines for log-linear approach in statistical machine translation. In Proceedings of the 6th International Workshop on Spoken Language Translation: Papers, pages 144–151, Tokyo, Japan.
- Cite (Informal):
- Structural support vector machines for log-linear approach in statistical machine translation (Hayashi et al., IWSLT 2009)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2009.iwslt-papers.3.pdf