@inproceedings{haque-etal-2020-terminology,
title = "Terminology-Aware Sentence Mining for {NMT} Domain Adaptation: {ADAPT}{'}s Submission to the Adap-{MT} 2020 {E}nglish-to-{H}indi {AI} Translation Shared Task",
author = "Haque, Rejwanul and
Moslem, Yasmin and
Way, Andy",
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.4",
pages = "17--23",
abstract = "This paper describes the ADAPT Centre{'}s submission to the Adap-MT 2020 AI Translation Shared Task for English-to-Hindi. The neural machine translation (NMT) systems that we built to translate AI domain texts are state-of-the-art Transformer models. In order to improve the translation quality of our NMT systems, we made use of both in-domain and out-of-domain data for training and employed different fine-tuning techniques for adapting our NMT systems to this task, e.g. mixed fine-tuning and on-the-fly self-training. For this, we mined parallel sentence pairs and monolingual sentences from large out-of-domain data, and the mining process was facilitated through automatic extraction of terminology from the in-domain data. This paper outlines the experiments we carried out for this task and reports the performance of our NMT systems on the evaluation test set.",
}
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%0 Conference Proceedings
%T Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task
%A Haque, Rejwanul
%A Moslem, Yasmin
%A Way, Andy
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task
%D 2020
%8 dec
%I NLP Association of India (NLPAI)
%C Patna, India
%F haque-etal-2020-terminology
%X This paper describes the ADAPT Centre’s submission to the Adap-MT 2020 AI Translation Shared Task for English-to-Hindi. The neural machine translation (NMT) systems that we built to translate AI domain texts are state-of-the-art Transformer models. In order to improve the translation quality of our NMT systems, we made use of both in-domain and out-of-domain data for training and employed different fine-tuning techniques for adapting our NMT systems to this task, e.g. mixed fine-tuning and on-the-fly self-training. For this, we mined parallel sentence pairs and monolingual sentences from large out-of-domain data, and the mining process was facilitated through automatic extraction of terminology from the in-domain data. This paper outlines the experiments we carried out for this task and reports the performance of our NMT systems on the evaluation test set.
%U https://aclanthology.org/2020.icon-adapmt.4
%P 17-23
Markdown (Informal)
[Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task](https://aclanthology.org/2020.icon-adapmt.4) (Haque et al., ICON 2020)
ACL