@inproceedings{akella-etal-2020-exploring,
title = "Exploring Pair-Wise {NMT} for {I}ndian Languages",
author = "Akella, Kartheek and
Allu, Sai Himal and
Suresh Ragupathi, Sridhar and
Singhal, Aman and
Khan, Zeeshan and
Jawahar, C.v. and
P. Namboodiri, Vinay",
editor = "Bhattacharyya, Pushpak and
Sharma, Dipti Misra and
Sangal, Rajeev",
booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2020",
address = "Indian Institute of Technology Patna, Patna, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2020.icon-main.59",
pages = "437--443",
abstract = "In this paper, we address the task of improving pair-wise machine translation for specific low resource Indian languages. Multilingual NMT models have demonstrated a reasonable amount of effectiveness on resource-poor languages. In this work, we show that the performance of these models can be significantly improved upon by using back-translation through a filtered back-translation process and subsequent fine-tuning on the limited pair-wise language corpora. The analysis in this paper suggests that this method can significantly improve multilingual models{'} performance over its baseline, yielding state-of-the-art results for various Indian languages.",
}
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<abstract>In this paper, we address the task of improving pair-wise machine translation for specific low resource Indian languages. Multilingual NMT models have demonstrated a reasonable amount of effectiveness on resource-poor languages. In this work, we show that the performance of these models can be significantly improved upon by using back-translation through a filtered back-translation process and subsequent fine-tuning on the limited pair-wise language corpora. The analysis in this paper suggests that this method can significantly improve multilingual models’ performance over its baseline, yielding state-of-the-art results for various Indian languages.</abstract>
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%0 Conference Proceedings
%T Exploring Pair-Wise NMT for Indian Languages
%A Akella, Kartheek
%A Allu, Sai Himal
%A Suresh Ragupathi, Sridhar
%A Singhal, Aman
%A Khan, Zeeshan
%A Jawahar, C.v.
%A P. Namboodiri, Vinay
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F akella-etal-2020-exploring
%X In this paper, we address the task of improving pair-wise machine translation for specific low resource Indian languages. Multilingual NMT models have demonstrated a reasonable amount of effectiveness on resource-poor languages. In this work, we show that the performance of these models can be significantly improved upon by using back-translation through a filtered back-translation process and subsequent fine-tuning on the limited pair-wise language corpora. The analysis in this paper suggests that this method can significantly improve multilingual models’ performance over its baseline, yielding state-of-the-art results for various Indian languages.
%U https://aclanthology.org/2020.icon-main.59
%P 437-443
Markdown (Informal)
[Exploring Pair-Wise NMT for Indian Languages](https://aclanthology.org/2020.icon-main.59) (Akella et al., ICON 2020)
ACL
- Kartheek Akella, Sai Himal Allu, Sridhar Suresh Ragupathi, Aman Singhal, Zeeshan Khan, C.v. Jawahar, and Vinay P. Namboodiri. 2020. Exploring Pair-Wise NMT for Indian Languages. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 437–443, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).