Abstract
The Joint Probability Model proposed by Marcu and Wong (2002) provides a probabilistic framework for modeling phrase-based statistical machine transla- tion (SMT). The model’s usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present a method of constraining the search space of the Joint Probability Model based on statistically and linguistically motivated word align- ments. This method reduces the complexity and size of the Joint Model and allows it to display performance superior to the standard phrase-based models for small amounts of training material.- Anthology ID:
- 2006.amta-papers.2
- Volume:
- Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers
- Month:
- August 8-12
- Year:
- 2006
- Address:
- Cambridge, Massachusetts, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 10–18
- Language:
- URL:
- https://aclanthology.org/2006.amta-papers.2
- DOI:
- Cite (ACL):
- Alexandra Birch, Chris Callison-Burch, and Miles Osborne. 2006. Constraining the Phrase-Based, Joint Probability Statistical Translation Model. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 10–18, Cambridge, Massachusetts, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Constraining the Phrase-Based, Joint Probability Statistical Translation Model (Birch et al., AMTA 2006)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2006.amta-papers.2.pdf