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      Statistical Machine Translation for Indian Languages: Mission Hindi 2

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          Abstract

          This paper presents Centre for Development of Advanced Computing Mumbai's (CDACM) submission to NLP Tools Contest on Statistical Machine Translation in Indian Languages (ILSMT) 2015 (collocated with ICON 2015). The aim of the contest was to collectively explore the effectiveness of Statistical Machine Translation (SMT) while translating within Indian languages and between English and Indian languages. In this paper, we report our work on all five language pairs, namely Bengali-Hindi (\bnhi), Marathi-Hindi (\mrhi), Tamil-Hindi (\tahi), Telugu-Hindi (\tehi), and English-Hindi (\enhi) for Health, Tourism, and General domains. We have used suffix separation, compound splitting and preordering prior to SMT training and testing.

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          A phrase-based, joint probability model for statistical machine translation

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            Statistical Machine Translation of German Compound Words

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              Author and article information

              Journal
              2016-10-25
              Article
              1610.08000
              1848a67d-7262-478b-97c8-5f370bcb6b02

              http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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              In the Proceedings of the 12th International Conference on Natural Language Processing (ICON 2015)
              4 pages, Published in the Proceedings of NLP Tools Contest: Statistical Machine Translation in Indian Languages
              cs.CL

              Theoretical computer science
              Theoretical computer science

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