Abstract
We introduce two document-level features to polish baseline sentence-level translations generated by a state-of-the-art statistical machine translation (SMT) system. One feature uses the word-embedding technique to model the relation between a sentence and its context on the target side; the other feature is a crisp document-level token-type ratio of target-side translations for source-side words to model the lexical consistency in translation. The weights of introduced features are tuned to optimize the sentence- and document-level metrics simultaneously on the basis of Pareto optimality. Experimental results on two different schemes with different corpora illustrate that the proposed approach can efficiently and stably integrate document-level information into a sentence-level SMT system. The best improvements were approximately 0.5 BLEU on test sets with statistical significance.- Anthology ID:
- 2014.amta-researchers.9
- Volume:
- Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
- Month:
- October 22-26
- Year:
- 2014
- Address:
- Vancouver, Canada
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 110–123
- Language:
- URL:
- https://aclanthology.org/2014.amta-researchers.9
- DOI:
- Cite (ACL):
- Chenchen Ding, Masao Utiyama, and Eiichiro Sumita. 2014. Document-level re-ranking with soft lexical and semantic features for statistical machine translation. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 110–123, Vancouver, Canada. Association for Machine Translation in the Americas.
- Cite (Informal):
- Document-level re-ranking with soft lexical and semantic features for statistical machine translation (Ding et al., AMTA 2014)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2014.amta-researchers.9.pdf