Tencent submission for WMT20 Quality Estimation Shared Task
Haijiang Wu, Zixuan Wang, Qingsong Ma, Xinjie Wen, Ruichen Wang, Xiaoli Wang, Yulin Zhang, Zhipeng Yao, Siyao Peng
Abstract
This paper presents Tencent’s submission to the WMT20 Quality Estimation (QE) Shared Task: Sentence-Level Post-editing Effort for English-Chinese in Task 2. Our system ensembles two architectures, XLM-based and Transformer-based Predictor-Estimator models. For the XLM-based Predictor-Estimator architecture, the predictor produces two types of contextualized token representations, i.e., masked XLM and non-masked XLM; the LSTM-estimator and Transformer-estimator employ two effective strategies, top-K and multi-head attention, to enhance the sentence feature representation. For Transformer-based Predictor-Estimator architecture, we improve a top-performing model by conducting three modifications: using multi-decoding in machine translation module, creating a new model by replacing the transformer-based predictor with XLM-based predictor, and finally integrating two models by a weighted average. Our submission achieves a Pearson correlation of 0.664, ranking first (tied) on English-Chinese.- Anthology ID:
- 2020.wmt-1.124
- 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:
- 1062–1067
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.124
- DOI:
- Bibkey:
- Cite (ACL):
- Haijiang Wu, Zixuan Wang, Qingsong Ma, Xinjie Wen, Ruichen Wang, Xiaoli Wang, Yulin Zhang, Zhipeng Yao, and Siyao Peng. 2020. Tencent submission for WMT20 Quality Estimation Shared Task. In Proceedings of the Fifth Conference on Machine Translation, pages 1062–1067, Online. Association for Computational Linguistics.
- Cite (Informal):
- Tencent submission for WMT20 Quality Estimation Shared Task (Wu et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.124.pdf
- Video:
- https://slideslive.com/38939609
Export citation
@inproceedings{wu-etal-2020-tencent-submission,
title = "Tencent submission for {WMT}20 Quality Estimation Shared Task",
author = "Wu, Haijiang and
Wang, Zixuan and
Ma, Qingsong and
Wen, Xinjie and
Wang, Ruichen and
Wang, Xiaoli and
Zhang, Yulin and
Yao, Zhipeng and
Peng, Siyao",
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-1.124",
pages = "1062--1067",
abstract = "This paper presents Tencent{'}s submission to the WMT20 Quality Estimation (QE) Shared Task: Sentence-Level Post-editing Effort for English-Chinese in Task 2. Our system ensembles two architectures, XLM-based and Transformer-based Predictor-Estimator models. For the XLM-based Predictor-Estimator architecture, the predictor produces two types of contextualized token representations, i.e., masked XLM and non-masked XLM; the LSTM-estimator and Transformer-estimator employ two effective strategies, top-K and multi-head attention, to enhance the sentence feature representation. For Transformer-based Predictor-Estimator architecture, we improve a top-performing model by conducting three modifications: using multi-decoding in machine translation module, creating a new model by replacing the transformer-based predictor with XLM-based predictor, and finally integrating two models by a weighted average. Our submission achieves a Pearson correlation of 0.664, ranking first (tied) on English-Chinese.",
}
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%0 Conference Proceedings %T Tencent submission for WMT20 Quality Estimation Shared Task %A Wu, Haijiang %A Wang, Zixuan %A Ma, Qingsong %A Wen, Xinjie %A Wang, Ruichen %A Wang, Xiaoli %A Zhang, Yulin %A Yao, Zhipeng %A Peng, Siyao %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 wu-etal-2020-tencent-submission %X This paper presents Tencent’s submission to the WMT20 Quality Estimation (QE) Shared Task: Sentence-Level Post-editing Effort for English-Chinese in Task 2. Our system ensembles two architectures, XLM-based and Transformer-based Predictor-Estimator models. For the XLM-based Predictor-Estimator architecture, the predictor produces two types of contextualized token representations, i.e., masked XLM and non-masked XLM; the LSTM-estimator and Transformer-estimator employ two effective strategies, top-K and multi-head attention, to enhance the sentence feature representation. For Transformer-based Predictor-Estimator architecture, we improve a top-performing model by conducting three modifications: using multi-decoding in machine translation module, creating a new model by replacing the transformer-based predictor with XLM-based predictor, and finally integrating two models by a weighted average. Our submission achieves a Pearson correlation of 0.664, ranking first (tied) on English-Chinese. %U https://aclanthology.org/2020.wmt-1.124 %P 1062-1067
Markdown (Informal)
[Tencent submission for WMT20 Quality Estimation Shared Task](https://aclanthology.org/2020.wmt-1.124) (Wu et al., WMT 2020)
- Tencent submission for WMT20 Quality Estimation Shared Task (Wu et al., WMT 2020)
ACL
- Haijiang Wu, Zixuan Wang, Qingsong Ma, Xinjie Wen, Ruichen Wang, Xiaoli Wang, Yulin Zhang, Zhipeng Yao, and Siyao Peng. 2020. Tencent submission for WMT20 Quality Estimation Shared Task. In Proceedings of the Fifth Conference on Machine Translation, pages 1062–1067, Online. Association for Computational Linguistics.