Abstract
We propose a source-side decoding sequence language model for phrase-based statistical machine translation. This model is a reordering model in the sense that it helps the decoder find the correct decoding sequence. The model uses word-aligned bilingual training data. We show improved translation quality of up to 1.34% BLEU and 0.54% TER using this model compared to three other widely used reordering models.- Anthology ID:
- 2010.amta-papers.22
- Volume:
- Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
- Month:
- October 31-November 4
- Year:
- 2010
- Address:
- Denver, Colorado, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2010.amta-papers.22
- DOI:
- Cite (ACL):
- Minwei Feng, Arne Mauser, and Hermann Ney. 2010. A Source-side Decoding Sequence Model for Statistical Machine Translation. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- A Source-side Decoding Sequence Model for Statistical Machine Translation (Feng et al., AMTA 2010)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2010.amta-papers.22.pdf