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

1
ADAPT Centre, School of Computing, Dublin City University, Dublin 9, Ireland
2
School of Computing, National College of Ireland, Dublin 1, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Phivos Mylonas
Digital 2021, 1(2), 86-102; https://doi.org/10.3390/digital1020007
Received: 12 January 2021 / Revised: 2 March 2021 / Accepted: 20 March 2021 / Published: 2 April 2021
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. View Full-Text
Keywords: machine translation; statistical machine translation; neural machine translation; terminology translation; low-resource machine translation; byte pair encoding machine translation; statistical machine translation; neural machine translation; terminology translation; low-resource machine translation; byte pair encoding
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MDPI and ACS Style

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. https://doi.org/10.3390/digital1020007

AMA Style

Ramesh A, Parthasarathy VB, Haque R, Way A. Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. Digital. 2021; 1(2):86-102. https://doi.org/10.3390/digital1020007

Chicago/Turabian Style

Ramesh, Akshai, Venkatesh Balavadhani Parthasarathy, Rejwanul Haque, and Andy Way. 2021. "Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil" Digital 1, no. 2: 86-102. https://doi.org/10.3390/digital1020007

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