Abstract
Sentence alignment plays an extremely important role in machine translation. Most of the hybrid approaches get either a bad recall or low precision. We tackle disadvantages of several novel sentence alignment approaches, which combine length-based and word correspondences. Word clustering is applied in our method in order to improve the quality of the sentence aligner, especially when dealing with the sparse data problem. Our approach overcomes the limits of previous hybrid methods and obtains both highly recall and reasonable precision rates.
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Trieu, HL., Nguyen, PT., Nguyen, KA. (2014). Improving Moore’s Sentence Alignment Method Using Bilingual Word Clustering. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-02741-8_14
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DOI: https://doi.org/10.1007/978-3-319-02741-8_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02740-1
Online ISBN: 978-3-319-02741-8
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