The ADAPT System Description for the WMT20 News Translation Task
Venkatesh Parthasarathy, Akshai Ramesh, Rejwanul Haque, Andy Way
Correct Metadata for
Abstract
This paper describes the ADAPT Centre’s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English. We present our machine translation (MT) systems that were built using the state-of-the-art neural MT (NMT) model, Transformer. We applied various strategies in order to improve our baseline MT systems, e.g. onolin- gual sentence selection for creating synthetic training data, mining monolingual sentences for adapting our MT systems to the task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Tamil and Tamil-to-English translation tasks.- Anthology ID:
- 2020.wmt-1.27
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 262–268
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.27/
- DOI:
- 10.18653/v1/2020.wmt-1.27
- Bibkey:
- Cite (ACL):
- Venkatesh Parthasarathy, Akshai Ramesh, Rejwanul Haque, and Andy Way. 2020. The ADAPT System Description for the WMT20 News Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 262–268, Online. Association for Computational Linguistics.
- Cite (Informal):
- The ADAPT System Description for the WMT20 News Translation Task (Parthasarathy et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.27.pdf
- Video:
- https://slideslive.com/38939616
Export citation
@inproceedings{parthasarathy-etal-2020-adapt,
title = "The {ADAPT} System Description for the {WMT}20 News Translation Task",
author = "Parthasarathy, Venkatesh and
Ramesh, Akshai and
Haque, Rejwanul and
Way, Andy",
editor = {Barrault, Lo{\"i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.27/",
doi = "10.18653/v1/2020.wmt-1.27",
pages = "262--268",
abstract = "This paper describes the ADAPT Centre{'}s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English. We present our machine translation (MT) systems that were built using the state-of-the-art neural MT (NMT) model, Transformer. We applied various strategies in order to improve our baseline MT systems, e.g. onolin- gual sentence selection for creating synthetic training data, mining monolingual sentences for adapting our MT systems to the task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Tamil and Tamil-to-English translation tasks."
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%0 Conference Proceedings %T The ADAPT System Description for the WMT20 News Translation Task %A Parthasarathy, Venkatesh %A Ramesh, Akshai %A Haque, Rejwanul %A Way, Andy %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F parthasarathy-etal-2020-adapt %X This paper describes the ADAPT Centre’s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English. We present our machine translation (MT) systems that were built using the state-of-the-art neural MT (NMT) model, Transformer. We applied various strategies in order to improve our baseline MT systems, e.g. onolin- gual sentence selection for creating synthetic training data, mining monolingual sentences for adapting our MT systems to the task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Tamil and Tamil-to-English translation tasks. %R 10.18653/v1/2020.wmt-1.27 %U https://aclanthology.org/2020.wmt-1.27/ %U https://doi.org/10.18653/v1/2020.wmt-1.27 %P 262-268
Markdown (Informal)
[The ADAPT System Description for the WMT20 News Translation Task](https://aclanthology.org/2020.wmt-1.27/) (Parthasarathy et al., WMT 2020)
- The ADAPT System Description for the WMT20 News Translation Task (Parthasarathy et al., WMT 2020)
ACL
- Venkatesh Parthasarathy, Akshai Ramesh, Rejwanul Haque, and Andy Way. 2020. The ADAPT System Description for the WMT20 News Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 262–268, Online. Association for Computational Linguistics.