Across SMT and NMT models for 11 Indian languages, SentencePiece gives the highest BLEU for most language pairs, while BPE wins in the multilingual model.
Machine Translation Approaches and Survey for Indian Languages
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abstract
In this study, we present an analysis regarding the performance of the state-of-art Phrase-based Statistical Machine Translation (SMT) on multiple Indian languages. We report baseline systems on several language pairs. The motivation of this study is to promote the development of SMT and linguistic resources for these language pairs, as the current state-of-the-art is quite bleak due to sparse data resources. The success of an SMT system is contingent on the availability of a large parallel corpus. Such data is necessary to reliably estimate translation probabilities. We report the performance of baseline systems translating from Indian languages (Bengali, Guajarati, Hindi, Malayalam, Punjabi, Tamil, Telugu and Urdu) into English with average 10% accurate results for all the language pairs.
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Comparative analysis of subword tokenization approaches for Indian languages
Across SMT and NMT models for 11 Indian languages, SentencePiece gives the highest BLEU for most language pairs, while BPE wins in the multilingual model.