@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",
    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://preview.aclanthology.org/ingest-emnlp/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."
}