Abstract
In Machine Translation, it is customary to compute the model score of a predicted hypothesis as a linear combination of multiple features, where each feature assesses a particular facet of the hypothesis. The choice of a linear combination is usually justified by the possibility of efficient inference (decoding); yet, the appropriateness of this simple combination scheme to the task at hand is rarely questioned. In this paper, we propose an approach that replaces the linear scoring function with a non-linear scoring function. To investigate the applicability of this approach, we rescore n-best lists generated with a conventional machine translation engine (using a linear scoring function for generating its hypotheses) with a non-linear scoring function learned using the learning-to-rank framework. Moderate, though consistent, gains in BLEU are demonstrated on the WMT’10, WMT’11 and WMT’12 test sets.- Anthology ID:
- 2012.amta-papers.17
- 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.17
- DOI:
- Cite (ACL):
- Artem Sokolov, Guillaume Wisniewski, and François Yvon. 2012. Non-linear n-best List Reranking with Few Features. 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):
- Non-linear n-best List Reranking with Few Features (Sokolov et al., AMTA 2012)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2012.amta-papers.17.pdf