Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task
Longyue Wang, Zhaopeng Tu, Xing Wang, Li Ding, Liang Ding, Shuming Shi
Abstract
This paper describes the Tencent AI Lab’s submission of the WMT 2020 shared task on chat translation in English-German. Our neural machine translation (NMT) systems are built on sentence-level, document-level, non-autoregressive (NAT) and pretrained models. We integrate a number of advanced techniques into our systems, including data selection, back/forward translation, larger batch learning, model ensemble, finetuning as well as system combination. Specifically, we proposed a hybrid data selection method to select high-quality and in-domain sentences from out-of-domain data. To better capture the source contexts, we exploit to augment NAT models with evolved cross-attention. Furthermore, we explore to transfer general knowledge from four different pre-training language models to the downstream translation task. In general, we present extensive experimental results for this new translation task. Among all the participants, our German-to-English primary system is ranked the second in terms of BLEU scores.- Anthology ID:
- 2020.wmt-1.60
- 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:
- 483–491
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.60
- DOI:
- Bibkey:
- Cite (ACL):
- Longyue Wang, Zhaopeng Tu, Xing Wang, Li Ding, Liang Ding, and Shuming Shi. 2020. Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 483–491, Online. Association for Computational Linguistics.
- Cite (Informal):
- Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task (Wang et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.60.pdf
- Video:
- https://slideslive.com/38939671
Export citation
@inproceedings{wang-etal-2020-tencent,
title = "Tencent {AI} Lab Machine Translation Systems for {WMT}20 Chat Translation Task",
author = "Wang, Longyue and
Tu, Zhaopeng and
Wang, Xing and
Ding, Li and
Ding, Liang and
Shi, Shuming",
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-1.60",
pages = "483--491",
abstract = "This paper describes the Tencent AI Lab{'}s submission of the WMT 2020 shared task on chat translation in English-German. Our neural machine translation (NMT) systems are built on sentence-level, document-level, non-autoregressive (NAT) and pretrained models. We integrate a number of advanced techniques into our systems, including data selection, back/forward translation, larger batch learning, model ensemble, finetuning as well as system combination. Specifically, we proposed a hybrid data selection method to select high-quality and in-domain sentences from out-of-domain data. To better capture the source contexts, we exploit to augment NAT models with evolved cross-attention. Furthermore, we explore to transfer general knowledge from four different pre-training language models to the downstream translation task. In general, we present extensive experimental results for this new translation task. Among all the participants, our German-to-English primary system is ranked the second in terms of BLEU scores.",
}
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%0 Conference Proceedings %T Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task %A Wang, Longyue %A Tu, Zhaopeng %A Wang, Xing %A Ding, Li %A Ding, Liang %A Shi, Shuming %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 wang-etal-2020-tencent %X This paper describes the Tencent AI Lab’s submission of the WMT 2020 shared task on chat translation in English-German. Our neural machine translation (NMT) systems are built on sentence-level, document-level, non-autoregressive (NAT) and pretrained models. We integrate a number of advanced techniques into our systems, including data selection, back/forward translation, larger batch learning, model ensemble, finetuning as well as system combination. Specifically, we proposed a hybrid data selection method to select high-quality and in-domain sentences from out-of-domain data. To better capture the source contexts, we exploit to augment NAT models with evolved cross-attention. Furthermore, we explore to transfer general knowledge from four different pre-training language models to the downstream translation task. In general, we present extensive experimental results for this new translation task. Among all the participants, our German-to-English primary system is ranked the second in terms of BLEU scores. %U https://aclanthology.org/2020.wmt-1.60 %P 483-491
Markdown (Informal)
[Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task](https://aclanthology.org/2020.wmt-1.60) (Wang et al., WMT 2020)
- Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task (Wang et al., WMT 2020)
ACL
- Longyue Wang, Zhaopeng Tu, Xing Wang, Li Ding, Liang Ding, and Shuming Shi. 2020. Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 483–491, Online. Association for Computational Linguistics.