Abstract
This system paper describes an end-to-end NMT pipeline for the Japanese ↔ English news translation task as submitted to WMT 2021, where we explore the efficacy of techniques such as tokenizing with language-independent and language-dependent tokenizers, normalizing by orthographic conversion, creating a politeness-and-formality-aware model by implementing a tagger, back-translation, model ensembling, and n-best reranking. We use parallel corpora provided by WMT 2021 organizers for training, and development and test data from WMT 2020 for evaluation of different experiment models. The preprocessed corpora are trained with a Transformer neural network model. We found that combining various techniques described herein, such as language-independent BPE tokenization, incorporating politeness and formality tags, model ensembling, n-best reranking, and back-translation produced the best translation models relative to other experiment systems.- Anthology ID:
- 2021.wmt-1.11
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 144–153
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.11
- DOI:
- Bibkey:
- Cite (ACL):
- Giang Le, Shinka Mori, and Lane Schwartz. 2021. Illinois Japanese ↔ English News Translation for WMT 2021. In Proceedings of the Sixth Conference on Machine Translation, pages 144–153, Online. Association for Computational Linguistics.
- Cite (Informal):
- Illinois Japanese ↔ English News Translation for WMT 2021 (Le et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.11.pdf
- Data
- WMT 2020
Export citation
@inproceedings{le-etal-2021-illinois,
title = "{I}llinois {J}apanese $\leftrightarrow$ {E}nglish {N}ews {T}ranslation for {WMT} 2021",
author = "Le, Giang and
Mori, Shinka and
Schwartz, Lane",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.11",
pages = "144--153",
abstract = "This system paper describes an end-to-end NMT pipeline for the Japanese $\leftrightarrow$ English news translation task as submitted to WMT 2021, where we explore the efficacy of techniques such as tokenizing with language-independent and language-dependent tokenizers, normalizing by orthographic conversion, creating a politeness-and-formality-aware model by implementing a tagger, back-translation, model ensembling, and n-best reranking. We use parallel corpora provided by WMT 2021 organizers for training, and development and test data from WMT 2020 for evaluation of different experiment models. The preprocessed corpora are trained with a Transformer neural network model. We found that combining various techniques described herein, such as language-independent BPE tokenization, incorporating politeness and formality tags, model ensembling, n-best reranking, and back-translation produced the best translation models relative to other experiment systems.",
}
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%0 Conference Proceedings %T Illinois Japanese łeftrightarrow English News Translation for WMT 2021 %A Le, Giang %A Mori, Shinka %A Schwartz, Lane %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F le-etal-2021-illinois %X This system paper describes an end-to-end NMT pipeline for the Japanese łeftrightarrow English news translation task as submitted to WMT 2021, where we explore the efficacy of techniques such as tokenizing with language-independent and language-dependent tokenizers, normalizing by orthographic conversion, creating a politeness-and-formality-aware model by implementing a tagger, back-translation, model ensembling, and n-best reranking. We use parallel corpora provided by WMT 2021 organizers for training, and development and test data from WMT 2020 for evaluation of different experiment models. The preprocessed corpora are trained with a Transformer neural network model. We found that combining various techniques described herein, such as language-independent BPE tokenization, incorporating politeness and formality tags, model ensembling, n-best reranking, and back-translation produced the best translation models relative to other experiment systems. %U https://aclanthology.org/2021.wmt-1.11 %P 144-153
Markdown (Informal)
[Illinois Japanese ↔ English News Translation for WMT 2021](https://aclanthology.org/2021.wmt-1.11) (Le et al., WMT 2021)
- Illinois Japanese ↔ English News Translation for WMT 2021 (Le et al., WMT 2021)
ACL
- Giang Le, Shinka Mori, and Lane Schwartz. 2021. Illinois Japanese ↔ English News Translation for WMT 2021. In Proceedings of the Sixth Conference on Machine Translation, pages 144–153, Online. Association for Computational Linguistics.