Constraining the Phrase-Based, Joint Probability Statistical Translation Model
29119Authors
nan
Abstract
AbstractThe 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.
Keywords
Full Content
nan
Labeled Text
AbstractThe 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.
Publication Details
- Paper ID:
- 29119
- Year:
- 2006
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan