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Statistical machine translation of Indian languages: a survey

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Abstract

In this study, performance analysis of a state-of-art phrase-based statistical machine translation (SMT) system is presented on eight Indian languages. State of the art in SMT on different Indian languages to English language has also been discussed briefly. The motivation of this study was to promote the development of SMT and linguistic resources for these Indian language pairs, as the current systems are in infancy stage due to sparse data resources. EMILLE and crowdsourcing parallel corpora have been used in this study for experimental purposes. The study is concluded by presenting the performance of baseline SMT system for Indian languages (Bengali, Gujarati, Hindi, Malayalam, Punjabi, Tamil, Telugu and Urdu) into English with average 10–20 % accurate results for all the language pairs. As a result of this study, both of these annotated parallel corpora resources and SMT system will serve as benchmarks for future approaches to SMT in Hindi → English, Urdu → English, Punjabi → English, Telugu → English, Tamil → English, Gujarati → English, Bengali → English and Malayalam → English.

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Acknowledgement

We would like to thank Dr. Nadir Durrani from University of Edinburgh for his helpful comments and suggestions during the experimentation and proof reading the write up, which has helped us a lot to improve the paper. He also provided examples to be included in the text.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad, Pakistan

    Nadeem Khan Jadoon

  2. Department of Computer Science, COMSATS Institute of Information Technology, Lahore, Pakistan

    Waqas Anwar, Usama Ijaz Bajwa & Farooq Ahmad

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  1. Nadeem Khan Jadoon
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  2. Waqas Anwar
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  3. Usama Ijaz Bajwa
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  4. Farooq Ahmad
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Correspondence to Waqas Anwar.

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Khan Jadoon, N., Anwar, W., Bajwa, U.I. et al. Statistical machine translation of Indian languages: a survey. Neural Comput & Applic 31, 2455–2467 (2019). https://doi.org/10.1007/s00521-017-3206-2

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  • DOI: https://doi.org/10.1007/s00521-017-3206-2

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