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

Ramesh, Akshai, Parthasarathy, Venkatesh, Haque, Rejwanul and Way, Andy (2021) Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. Digital, 1 (2). pp. 86-102. ISSN 2673-6470

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Abstract

Phrase-based statistical machine translation (PB-SMT) has been the dominant paradigm in machine translation (MT) research for more than two decades. Deep neural MT models have been producing state-of-the-art performance across many translation tasks for four to five years. To put it another way, neural MT (NMT) took the place of PB-SMT a few years back and currently represents the state-of-the-art in MT research. Translation to or from under-resourced languages has been historically seen as a challenging task. Despite producing state-of-the-art results in many translation tasks, NMT still poses many problems such as performing poorly for many low-resource language pairs mainly because of its learning task’s data-demanding nature. MT researchers have been trying to address this problem via various techniques, e.g., exploiting source- and/or target-side monolingual data for training, augmenting bilingual training data, and transfer learning. Despite some success, none of the present-day benchmarks have entirely overcome the problem of translation in low-resource scenarios for many languages. In this work, we investigate the performance of PB-SMT and NMT on two rarely tested under-resourced language pairs, English-To-Tamil and Hindi-To-Tamil, taking a specialised data domain into consideration. This paper demonstrates our findings and presents results showing the rankings of our MT systems produced via a social media-based human evaluation scheme.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Mr Kevin Loughran
Date Deposited:	20 May 2021 11:10
Last Modified:	20 May 2021 11:10
URI:	https://norma.ncirl.ie/id/eprint/4817

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