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Investigating low­-resource machine translation for English­-to­-Tamil

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).

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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(SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT.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 neural MT (NMT) on a rarely-tested low-resource language-pair, English-to-Tamil, taking a specialised data domain (software localisation) into consideration. In particular, we produce rankings of our MT systems via a social media platform-based human evaluation scheme, and demonstrate our findings in the low-resource domain-specific text translation task.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Additional Information:Part of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).
Subjects:Computer Science > Computational linguistics
Computer Science > Computer engineering
Computer Science > Machine learning
Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages (LoResMT 2020). .
Official URL:https://aclanthology.org/2020.loresmt-1.15
Copyright Information:© 2020 The Authors.
Funders:Science Foundation of Ireland, Grant No. 13/RC/2106, European Research Council, European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska­Curie grant agreement No. 713567, Science Foundation of Ireland, Grant No. 13/RC/2077
ID Code:25201
Deposited On:04 Dec 2020 14:23 by Rejwanul Haque . Last Modified 07 Jan 2022 16:54

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