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An error-based investigation of statistical and neural machine translation performance on Hindi-to-Tamil and English-to-Tamil

Ramesh, Akshai, Parthasarathy, Venkatesh Balavadhani, Haque, Rejwanul orcid logoORCID: 0000-0003-1680-0099 and Way, Andy orcid logoORCID: 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).

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(SMT) was the state-of-the-art in machine translation (MT) research for more than two decades, but has since been superseded by neural MT (NMT).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.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Subjects:Computer Science > Artificial intelligence
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 Institutes and Centres > ADAPT
Published in: Proceedings of the 7th Workshop on Asian Translation (WAT2020). . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://www.aclweb.org/anthology/2020.wat-1.22
Copyright Information:© 2020 The Authors.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) Research Centres Programme (Grant No. 13/RC/2106), European Research Council, European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713567, Science Foundation Ireland (SFI) under Grant Number 13/RC/2077
ID Code:25203
Deposited On:04 Dec 2020 14:24 by Rejwanul Haque . Last Modified 05 Dec 2023 15:23

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