Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning
Haluk Açarçiçek, Talha Çolakoğlu, Pınar Ece Aktan Hatipoğlu, Chong Hsuan Huang, Wei Peng
Abstract
This paper illustrates Huawei’s submission to the WMT20 low-resource parallel corpus filtering shared task. Our approach focuses on developing a proxy task learner on top of a transformer-based multilingual pre-trained language model to boost the filtering capability for noisy parallel corpora. Such a supervised task also helps us to iterate much more quickly than using an existing neural machine translation system to perform the same task. After performing empirical analyses of the finetuning task, we benchmark our approach by comparing the results with past years’ state-of-theart records. This paper wraps up with a discussion of limitations and future work. The scripts for this study will be made publicly available.- Anthology ID:
- 2020.wmt-1.105
- 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:
- 940–946
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.105
- DOI:
- Bibkey:
- Cite (ACL):
- Haluk Açarçiçek, Talha Çolakoğlu, Pınar Ece Aktan Hatipoğlu, Chong Hsuan Huang, and Wei Peng. 2020. Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning. In Proceedings of the Fifth Conference on Machine Translation, pages 940–946, Online. Association for Computational Linguistics.
- Cite (Informal):
- Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning (Açarçiçek et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.105.pdf
- Video:
- https://slideslive.com/38939606
- Code
- wpti/proxy-filter
Export citation
@inproceedings{acarcicek-etal-2020-filtering,
title = "Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning",
author = "A{\c{c}}ar{\c{c}}i{\c{c}}ek, Haluk and
{\c{C}}olako{\u{g}}lu, Talha and
Aktan Hatipo{\u{g}}lu, P{\i}nar Ece and
Huang, Chong Hsuan and
Peng, Wei",
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-1.105",
pages = "940--946",
abstract = "This paper illustrates Huawei{'}s submission to the WMT20 low-resource parallel corpus filtering shared task. Our approach focuses on developing a proxy task learner on top of a transformer-based multilingual pre-trained language model to boost the filtering capability for noisy parallel corpora. Such a supervised task also helps us to iterate much more quickly than using an existing neural machine translation system to perform the same task. After performing empirical analyses of the finetuning task, we benchmark our approach by comparing the results with past years{'} state-of-theart records. This paper wraps up with a discussion of limitations and future work. The scripts for this study will be made publicly available.",
}
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%0 Conference Proceedings %T Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning %A Açarçiçek, Haluk %A Çolakoğlu, Talha %A Aktan Hatipoğlu, Pınar Ece %A Huang, Chong Hsuan %A Peng, Wei %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 acarcicek-etal-2020-filtering %X This paper illustrates Huawei’s submission to the WMT20 low-resource parallel corpus filtering shared task. Our approach focuses on developing a proxy task learner on top of a transformer-based multilingual pre-trained language model to boost the filtering capability for noisy parallel corpora. Such a supervised task also helps us to iterate much more quickly than using an existing neural machine translation system to perform the same task. After performing empirical analyses of the finetuning task, we benchmark our approach by comparing the results with past years’ state-of-theart records. This paper wraps up with a discussion of limitations and future work. The scripts for this study will be made publicly available. %U https://aclanthology.org/2020.wmt-1.105 %P 940-946
Markdown (Informal)
[Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning](https://aclanthology.org/2020.wmt-1.105) (Açarçiçek et al., WMT 2020)
- Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning (Açarçiçek et al., WMT 2020)
ACL
- Haluk Açarçiçek, Talha Çolakoğlu, Pınar Ece Aktan Hatipoğlu, Chong Hsuan Huang, and Wei Peng. 2020. Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning. In Proceedings of the Fifth Conference on Machine Translation, pages 940–946, Online. Association for Computational Linguistics.