Abstract
In this paper, we propose a novel lattice-based MT combination methodology that we call Target-to-Target Decoding (TTD). The combination process is carried out as a “translation” from backbone to the combination result. This perspective suggests the use of existing phrase-based MT techniques in the combination framework. We show how phrase extraction rules and confidence estimations inspired from machine translation improve results. We also propose system-specific LMs for estimating N-gram consensus. Our results show that our approach yields a strong improvement over the best single MT system and competes with other state-of-the-art combination systems.- Anthology ID:
- 2012.amta-papers.11
- 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.11
- DOI:
- Cite (ACL):
- Wei-Yun Ma and Kathleen McKeown. 2012. Phrase-level System Combination for Machine Translation Based on Target-to-Target Decoding. 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):
- Phrase-level System Combination for Machine Translation Based on Target-to-Target Decoding (Ma & McKeown, AMTA 2012)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2012.amta-papers.11.pdf