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Investigating low­-resource machine translation for English­-to­-Tamil
In: Ramesh, Akshai, Parthasarathy, Venkatesh Balavadhani, Haque, Rejwanul orcid:0000-0003-1680-0099 and Way, Andy orcid:0000-0001-5736-5930 (2020) Investigating low­-resource machine translation for English­-to­-Tamil. In: Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages (LoResMT 2020) AACL-IJCNLP, December 4-7, 2020, Suzhou, China (Online). (2020)
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An error-based investigation of statistical and neural machine translation performance on Hindi-to-Tamil and English-to-Tamil
In: Ramesh, Akshai, Parthasarathy, Venkatesh Balavadhani, Haque, Rejwanul orcid:0000-0003-1680-0099 and Way, Andy orcid:0000-0001-5736-5930 (2020) An error-based investigation of statistical and neural machine translation performance on Hindi-to-Tamil and English-to-Tamil. In: 7th Workshop on Asian Translation (WAT2020), 4 Dec 2020, Suzhou, China (Online). (2020)
Abstract: Statistical machine translation (SMT) was the state-of-the-art in machine translation (MT) research for more than two decades, but has since been superseded by neural MT (NMT). Despite producing state-of-the-art results in many translation tasks, neural models underperform in 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.
Keyword: Artificial intelligence; Computational linguistics; Computer engineering; Machine learning; Machine translating
URL: http://doras.dcu.ie/25203/
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The ADAPT system description for the WMT20 news translation task
In: Parthasarathy, Venkatesh Balavadhani, Ramesh, Akshai, Haque, Rejwanul orcid:0000-0003-1680-0099 and Way, Andy orcid:0000-0001-5736-5930 (2020) The ADAPT system description for the WMT20 news translation task. In: Fifth Conference on Machine Translation (NEWS Shared Task), 19 -20 Nov 2020, Dominican Republic (Online). (2020)
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