Difference between revisions of "Vogal et al, COLING 1996"
From Cohen Courses
Jump to navigationJump to search| Line 16: | Line 16: | ||
== Algorithm == | == Algorithm == | ||
| − | <math> | + | <math> |
| + | While IBM Model 2 attempts to model the absolute distortion of words in sentence pairs $Pr_a(i|j,J,I)$, alignments have a strong tendency to maintain the local neighborhood after translation. | ||
| + | </math> | ||
Revision as of 12:32, 23 September 2011
Contents
Citation
Vogel, S., Ney, H., & Tillmann, C. (1996). Hmm-based word alignment in statistical translation. In Proceedings of the 16th conference on Computational linguistics - Volume 2, COLING ’96, pp. 836–841, Stroudsburg, PA, USA. Association for Computational Linguistics.
Online version
Summary
Word Alignments map the word correspondence between two parallel sentences in different languages.
This work extends IBM models 1 and 2, which models lexical translation probabilities and absolute distortion probabilities, by also modeling relative distortion.
The relative distortion is modeled by applying a first-order HMM, where each alignment probabilities are dependent on the distortion of the previous alignment.
Algorithm
