Syntactic re-alignment models for machine translation (2007)
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| Venue: | In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL-2007 |
| Citations: | 9 - 2 self |
BibTeX
@INPROCEEDINGS{Knight07syntacticre-alignment,
author = {Kevin Knight},
title = {Syntactic re-alignment models for machine translation},
booktitle = {In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL-2007},
year = {2007}
}
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Abstract
We present a method for improving word alignment for statistical syntax-based machine translation that employs a syntactically informed alignment model closer to the translation model than commonly-used word alignment models. This leads to extraction of more useful linguistic patterns and improved BLEU scores on translation experiments in Chinese and Arabic. 1 Methods of statistical MT Roughly speaking, there are two paths commonly taken in statistical machine translation (Figure 1). The idealistic path uses an unsupervised learning algorithm such as EM (Demptser et al., 1977) to learn parameters for some proposed translation model from a bitext training corpus, and then directly translates using the weighted model. Some examples of the idealistic approach are the direct IBM word model (Berger et al., 1994; Germann et al., 2001), the phrase-based approach of Marcu and Wong (2002), and the syntax approaches of Wu (1996) and Yamada and Knight (2001). Idealistic approaches are conceptually simple and thus easy to relate to observed phenomena. However, as more parameters are added to the model the idealistic approach has not scaled well, for it is increasingly difficult to incorporate large amounts of training data efficiently over an increasingly large search space. Additionally, the EM procedure has a tendency to overfit its training data when the input units have varying explanatory powers, such as variable-size phrases or variable-height trees.
