Abstract
We present a phrase-based statistical machine translation (SMT) system for Bangla to English that incorporates a novel transliteration module, and a specialized component for handling prepositions and Bangla compound words. We evaluate our components through their impact on the BLEU score for the phrase-based SMT system. According to the experimental results, the transliteration component has the most significant impact on the BLEU score. We also provide a new test set with multiple references between Bangla and English for MT evaluation purposes. Finally we propose a new manual evaluation approach for the MT community and evaluate our components using the new manual evaluation approach.
This research was partially supported by a discovery grant from NSERC, Canada.
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Roy, M., Popowich, F. (2010). Phrase-Based Statistical Machine Translation for a Low-Density Language Pair. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_27
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DOI: https://doi.org/10.1007/978-3-642-13059-5_27
Publisher Name: Springer, Berlin, Heidelberg
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