Lexical translation model that considers word order.
IBM Model 2 improves on Model 1 by accounting for word order. An alignment probability is introduced, a(i | j,l,m), which predicts a source word position, given its aligned target word's position.
The EM algorithm used in Model 2 is: E step - In the training data, collect counts, weighted by prior
probabilities. (a) count how many times a source language word is translated
into a target language word
- count how many times a particular position in the source sentence is aligned to a particular position in the target sentence
M step - Estimate new probabilities based on the counts from the E step
Notations: i: Position in the source sentence
Valid values are 0 (for NULL), 1, 2, ..., length of source sentence
- j: Position in the target sentence
- Valid values are 1, 2, ..., length of target sentence
l: Number of words in the source sentence, excluding NULL m: Number of words in the target sentence s: A word in the source language t: A word in the target language
References: Philipp Koehn. 2010. Statistical Machine Translation. Cambridge University Press, New York.
Peter E Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics, 19 (2), 263-311.
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Lexical translation model that considers word order |
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Data object to store counts of various parameters during training. Includes counts for alignment. |