In India Language Translation systems have been developed for translation from English to Indian Languages and from regional languages to regional languages. These systems are also used for teaching machine translation to the students and researchers. Most of these systems are in the English to Hindi domain with exceptions of a Hindi to English and English to Kannada machine translation system. English is a SVO (subject-verb-object) language while Indian regional languages are SOV (subject-object-verb) and are relatively of free word-order. A survey of the machine translation systems that have been developed in media for translation from English to Indian languages and among Indian languages reveals that the machine translation software is used in field testing or is available as web translation service. We present an analysis regarding the performance of the state-of-art Phrase-based Language Translation (LT) on Indian Sindhi languages. We report baseline systems on Sindhi language pairs. The motivation of this study is to promote the development of SLT 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 SLT system is contingent on the availability of a large parallel corpus i.e. Dictionary. Such data is necessary to reliably estimate translation probabilities.

Statistical Language Translation (SLT), Phrase-based Translation, Parallel Corpus, Natural Language Processing (NLP), Sindhi Phrase Word By Word

[1] NadeemJadoon Khan, Waqas Anwar & Nadir Durrani, “Machine Translation Approaches and Survey for Indian Languages”,2017.
[2] ALPAC “Language and Machines: Computers in Translation and Linguistics”.A report by the Automatic Language Processing Advisory Committee (Tech. Rep. No. Publication 1416), 2101 Constitution Avenue, Washington D.C., 20418 USA: National Academy of Sciences, National Research Council, 1966.
[3]Balajapally, P., Bandaru, P., Ganapathiraju, M., Balakrishnan, N., & Reddy, R., “Multilingual Book Reader: Transliteration, Word-to-Word Translation and Full-text Translation”, 2006.
[4] Dwivedi, S. K., &Sukhadeve, P. P.,“Machine Translation System in Indian Perspectives”,Journal of Computer Science, 6(10), 1111-1116, 2010.
[5] Antony P. J.,“Machine Translation Approaches and Survey for Indian Languages. Computational Linguistics and Chinese Language Processing”, Vol. 18, No. 1, March 2013, pp. 47-78.
[6] Hasler, E., Haddow B., and Koehn, P.,“Sparse lexicalised features and topic adaptation for SMT”,In Proceedings of the seventh International Workshop on Spoken Language Translation, pages 268–275, 2012.
[7] Och, F., “A systematic comparison of various statistical alignment models. Computational Linguistics”, 29(1):19–5, 2003.
[8] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu, “BLEU: a Method for Automatic Evaluation of Machine Translation”, In Proceedings of 40th Annual meeting of the Association for Computational Linguistics (ACL), Philadelphia, July 2002, pp. 311–318.
[9] Bisazza, A. and Federico, M., “Chunk-based verb reordering in VSO sentences for Arabic-English statistical machine translation”, In Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and Metrics MATR, WMT ’10,15-16 july 2010, pp. 235–243.
[10] J. Eisner, “Learning non-isomorphic tree mappings for machine translation”, In Proceedings of the ACL Interactive Poster/Demonstration Sessions, 2003, 205–208.
[11] Dash, NiladriSekhar, Chaudhuri, BidyutBaran, “Why do we need to develop corpora in Indian languages?” A paper presented at SCALLA 2001 conference, Bangalore.
[12] Rao, Durgesh, “Machine Translation in India: A Brief Survey”, SCALLA 2001 conference, Bangalore.
[13] Naskar, S., &Bandyopadhyay, S,“Use of Machine Translation in India: Current Status”, In Proceedings of MT SUMMIT X; September 13-15, 2005, Phuket, Thailand.
[14] Bandyopadhyay S., “ANUBAAD - The Translator from English to Indian Languages” In proceedings of the VIIth State Science and Technology Congress, Calcutta. India, 2000, pp. 43-51.