Ramesh, A.; Parthasarathy, V.B.; Haque, R.; Way, A. Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. Digital2021, 1, 86-102.
Ramesh, A.; Parthasarathy, V.B.; Haque, R.; Way, A. Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. Digital 2021, 1, 86-102.
Ramesh, A.; Parthasarathy, V.B.; Haque, R.; Way, A. Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. Digital2021, 1, 86-102.
Ramesh, A.; Parthasarathy, V.B.; Haque, R.; Way, A. Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. Digital 2021, 1, 86-102.
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(SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT.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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
