Abstract
Reordering is of essential importance problem for phrase based statistical machine translation (SMT). In this paper, we propose an approach to automatically learn reordering rules as preprocessing step based on a dependency parser in phrase-based statistical machine translation for English to Vietnamese. We used dependency parsing and rules extracting from training the features-rich discriminative classifiers for reordering source-side sentences. We evaluated our approach on English-Vietnamese machine translation tasks, and showed that it outperform the baseline phrase-based SMT system.
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Acknowledgements
This work described in this paper has been partially funded by Hanoi National University (QG.15.23 project).
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Tran, V.H., Vu, H.T., Nguyen, V.V., Nguyen, M.L. (2018). A Classifier-Based Preordering Approach for English-Vietnamese Statistical Machine Translation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_7
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