@inproceedings{ala-sharma-2020-adapnmt,
title = "{A}dap{NMT} : Neural Machine Translation with Technical Domain Adaptation for Indic Languages",
author = "Ala, Hema and
Sharma, Dipti",
editor = "Sharma, Dipti Misra and
Ekbal, Asif and
Arora, Karunesh and
Naskar, Sudip Kumar and
Ganguly, Dipankar and
L, Sobha and
Mamidi, Radhika and
Arora, Sunita and
Mishra, Pruthwik and
Mujadia, Vandan",
booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task",
month = dec,
year = "2020",
address = "Patna, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2020.icon-adapmt.2",
pages = "6--10",
abstract = "Adapting new domain is highly challenging task for Neural Machine Translation (NMT). In this paper we show the capability of general domain machine translation when translating into Indic languages (English - Hindi , English - Telugu and Hindi - Telugu), and low resource domain adaptation of MT systems using existing general parallel data and small in domain parallel data for AI and Chemistry Domains. We carried out our experiments using Byte Pair Encoding(BPE) as it solves rare word problems. It has been observed that with addition of little amount of in-domain data to the general data improves the BLEU score significantly.",
}
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%0 Conference Proceedings
%T AdapNMT : Neural Machine Translation with Technical Domain Adaptation for Indic Languages
%A Ala, Hema
%A Sharma, Dipti
%Y Sharma, Dipti Misra
%Y Ekbal, Asif
%Y Arora, Karunesh
%Y Naskar, Sudip Kumar
%Y Ganguly, Dipankar
%Y L, Sobha
%Y Mamidi, Radhika
%Y Arora, Sunita
%Y Mishra, Pruthwik
%Y Mujadia, Vandan
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Patna, India
%F ala-sharma-2020-adapnmt
%X Adapting new domain is highly challenging task for Neural Machine Translation (NMT). In this paper we show the capability of general domain machine translation when translating into Indic languages (English - Hindi , English - Telugu and Hindi - Telugu), and low resource domain adaptation of MT systems using existing general parallel data and small in domain parallel data for AI and Chemistry Domains. We carried out our experiments using Byte Pair Encoding(BPE) as it solves rare word problems. It has been observed that with addition of little amount of in-domain data to the general data improves the BLEU score significantly.
%U https://aclanthology.org/2020.icon-adapmt.2
%P 6-10
Markdown (Informal)
[AdapNMT : Neural Machine Translation with Technical Domain Adaptation for Indic Languages](https://aclanthology.org/2020.icon-adapmt.2) (Ala & Sharma, ICON 2020)
ACL