Abstract
An adaptable statistical or hybrid MT system relies heavily on the quality of word-level alignments of real-world data. Statistical alignment approaches provide a reasonable initial estimate for word alignment. However, they cannot handle certain types of linguistic phenomena such as long-distance dependencies and structural differences between languages. We address this issue in Multi-Align, a new framework for incremental testing of different alignment algorithms and their combinations. Our design allows users to tune their systems to the properties of a particular genre/domain while still benefiting from general linguistic knowledge associated with a language pair. We demonstrate that a combination of statistical and linguistically-informed alignments can resolve translation divergences during the alignment process.- Anthology ID:
- 2004.amta-papers.3
- Volume:
- Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers
- Month:
- September 28 - October 2
- Year:
- 2004
- Address:
- Washington, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Springer
- Note:
- Pages:
- 17–26
- Language:
- URL:
- https://link.springer.com/chapter/10.1007/978-3-540-30194-3_3
- DOI:
- Cite (ACL):
- Necip Fazil Ayan, Bonnie Dorr, and Nizar Habash. 2004. Multi-Align: combining linguistic and statistical techniques to improve alignments for adaptable MT. In Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 17–26, Washington, USA. Springer.
- Cite (Informal):
- Multi-Align: combining linguistic and statistical techniques to improve alignments for adaptable MT (Ayan et al., AMTA 2004)
- PDF:
- https://link.springer.com/chapter/10.1007/978-3-540-30194-3_3