Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparing Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil

Version 1 : Received: 22 December 2020 / Approved: 23 December 2020 / Online: 23 December 2020 (09:52:33 CET)

How to cite: Ramesh, A.; Parthasarathy, V.B.; Way, A.; Haque, R. Comparing Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil. Preprints 2020, 2020120580 (doi: 10.20944/preprints202012.0580.v1). Ramesh, A.; Parthasarathy, V.B.; Way, A.; Haque, R. Comparing Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil. Preprints 2020, 2020120580 (doi: 10.20944/preprints202012.0580.v1).

Abstract

Statistical machine translation (SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT. Although deep neural models produce state-of-the-art results in many translation tasks, they are found to under-perform on resource-poor scenarios. Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages. In this work, we investigate the performance of phrase-based SMT (PB-SMT) and NMT on two rarely-tested low-resource language-pairs, English-to-Tamil and Hindi-to-Tamil, taking a specialised data domain (software localisation) into consideration. This paper demonstrates our findings including the identification of several issues of the current neural approaches to low-resource domain-specific text translation and rankings of our MT systems via a social media platform-based human evaluation scheme.

Subject Areas

Machine Translation; Statistical Machine Translation; Neural Machine Translation; Terminology Translation; Low-resource Machine Translation; Byte-Pair Encoding

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