Statistical Machine Translation for Indian Languages: Mission Hindi 2
This work addresses machine translation challenges for Indian languages, but it is incremental as it applies existing methods to new language pairs and domains.
The paper tackled the problem of statistical machine translation for five language pairs involving Hindi and other Indian languages or English, reporting results from a contest submission using techniques like suffix separation and preordering.
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.