@inproceedings{hira-etal-2019-exploring,
title = "Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation",
author = "Hira, Noor-e- and
Abdul Rauf, Sadaf and
Kiani, Kiran and
Zafar, Ammara and
Nawaz, Raheel",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5419",
doi = "10.18653/v1/W19-5419",
pages = "156--163",
abstract = "Transfer Learning and Selective data training are two of the many approaches being extensively investigated to improve the quality of Neural Machine Translation systems. This paper presents a series of experiments by applying transfer learning and selective data training for participation in the Bio-medical shared task of WMT19. We have used Information Retrieval to selectively choose related sentences from out-of-domain data and used them as additional training data using transfer learning. We also report the effect of tokenization on translation model performance.",
}
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%0 Conference Proceedings
%T Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation
%A Hira, Noor-e-
%A Abdul Rauf, Sadaf
%A Kiani, Kiran
%A Zafar, Ammara
%A Nawaz, Raheel
%S Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F hira-etal-2019-exploring
%X Transfer Learning and Selective data training are two of the many approaches being extensively investigated to improve the quality of Neural Machine Translation systems. This paper presents a series of experiments by applying transfer learning and selective data training for participation in the Bio-medical shared task of WMT19. We have used Information Retrieval to selectively choose related sentences from out-of-domain data and used them as additional training data using transfer learning. We also report the effect of tokenization on translation model performance.
%R 10.18653/v1/W19-5419
%U https://aclanthology.org/W19-5419
%U https://doi.org/10.18653/v1/W19-5419
%P 156-163
Markdown (Informal)
[Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation](https://aclanthology.org/W19-5419) (Hira et al., WMT 2019)
ACL