Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation
Noor-e- Hira, Sadaf Abdul Rauf, Kiran Kiani, Ammara Zafar, Raheel Nawaz
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.- Anthology ID:
- W19-5419
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 156–163
- Language:
- URL:
- https://aclanthology.org/W19-5419
- DOI:
- 10.18653/v1/W19-5419
- Bibkey:
- Cite (ACL):
- Noor-e- Hira, Sadaf Abdul Rauf, Kiran Kiani, Ammara Zafar, and Raheel Nawaz. 2019. Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 156–163, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation (Hira et al., WMT 2019)
- Copy Citation:
- PDF:
- https://aclanthology.org/W19-5419.pdf
Export citation
@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",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
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 %Y Bojar, Ondřej %Y Chatterjee, Rajen %Y Federmann, Christian %Y Fishel, Mark %Y Graham, Yvette %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Turchi, Marco %Y Verspoor, Karin %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)
- Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation (Hira et al., WMT 2019)
ACL
- Noor-e- Hira, Sadaf Abdul Rauf, Kiran Kiani, Ammara Zafar, and Raheel Nawaz. 2019. Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 156–163, Florence, Italy. Association for Computational Linguistics.