Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task

Rejwanul Haque, Yasmin Moslem, Andy Way


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.
Anthology ID:
2020.icon-adapmt.4
Volume:
Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task
Month:
December
Year:
2020
Address:
Patna, India
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
17–23
Language:
URL:
https://aclanthology.org/2020.icon-adapmt.4
DOI:
Bibkey:
Cite (ACL):
Rejwanul Haque, Yasmin Moslem, and Andy Way. 2020. Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task. In Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task, pages 17–23, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task (Haque et al., ICON 2020)
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PDF:
https://aclanthology.org/2020.icon-adapmt.4.pdf
Data
IndicNLP Corpus