Abstract
This paper describes the Alibaba Machine Translation Group submissions to the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment. In the filtering task, three main methods are applied to evaluate the quality of the parallel corpus, i.e. a) Dual Bilingual GPT-2 model, b) Dual Conditional Cross-Entropy Model and c) IBM word alignment model. The scores of these models are combined by using a positive-unlabeled (PU) learning model and a brute-force search to obtain additional gains. Besides, a few simple but efficient rules are adopted to evaluate the quality and the diversity of the corpus. In the alignment-filtering task, the extraction pipeline of bilingual sentence pairs includes the following steps: bilingual lexicon mining, language identification, sentence segmentation and sentence alignment. The final result shows that, in both filtering and alignment tasks, our system significantly outperforms the LASER-based system.- Anthology ID:
- 2020.wmt-1.111
- 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:
- 979–984
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.111
- DOI:
- Bibkey:
- Cite (ACL):
- Jun Lu, Xin Ge, Yangbin Shi, and Yuqi Zhang. 2020. Alibaba Submission to the WMT20 Parallel Corpus Filtering Task. In Proceedings of the Fifth Conference on Machine Translation, pages 979–984, Online. Association for Computational Linguistics.
- Cite (Informal):
- Alibaba Submission to the WMT20 Parallel Corpus Filtering Task (Lu et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.111.pdf
- Video:
- https://slideslive.com/38939580
Export citation
@inproceedings{lu-etal-2020-alibaba, title = "{A}libaba Submission to the {WMT}20 Parallel Corpus Filtering Task", author = "Lu, Jun and Ge, Xin and Shi, Yangbin and Zhang, Yuqi", 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-1.111", pages = "979--984", abstract = "This paper describes the Alibaba Machine Translation Group submissions to the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment. In the filtering task, three main methods are applied to evaluate the quality of the parallel corpus, i.e. a) Dual Bilingual GPT-2 model, b) Dual Conditional Cross-Entropy Model and c) IBM word alignment model. The scores of these models are combined by using a positive-unlabeled (PU) learning model and a brute-force search to obtain additional gains. Besides, a few simple but efficient rules are adopted to evaluate the quality and the diversity of the corpus. In the alignment-filtering task, the extraction pipeline of bilingual sentence pairs includes the following steps: bilingual lexicon mining, language identification, sentence segmentation and sentence alignment. The final result shows that, in both filtering and alignment tasks, our system significantly outperforms the LASER-based system.", }
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%0 Conference Proceedings %T Alibaba Submission to the WMT20 Parallel Corpus Filtering Task %A Lu, Jun %A Ge, Xin %A Shi, Yangbin %A Zhang, Yuqi %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 lu-etal-2020-alibaba %X This paper describes the Alibaba Machine Translation Group submissions to the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment. In the filtering task, three main methods are applied to evaluate the quality of the parallel corpus, i.e. a) Dual Bilingual GPT-2 model, b) Dual Conditional Cross-Entropy Model and c) IBM word alignment model. The scores of these models are combined by using a positive-unlabeled (PU) learning model and a brute-force search to obtain additional gains. Besides, a few simple but efficient rules are adopted to evaluate the quality and the diversity of the corpus. In the alignment-filtering task, the extraction pipeline of bilingual sentence pairs includes the following steps: bilingual lexicon mining, language identification, sentence segmentation and sentence alignment. The final result shows that, in both filtering and alignment tasks, our system significantly outperforms the LASER-based system. %U https://aclanthology.org/2020.wmt-1.111 %P 979-984
Markdown (Informal)
[Alibaba Submission to the WMT20 Parallel Corpus Filtering Task](https://aclanthology.org/2020.wmt-1.111) (Lu et al., WMT 2020)
- Alibaba Submission to the WMT20 Parallel Corpus Filtering Task (Lu et al., WMT 2020)
ACL
- Jun Lu, Xin Ge, Yangbin Shi, and Yuqi Zhang. 2020. Alibaba Submission to the WMT20 Parallel Corpus Filtering Task. In Proceedings of the Fifth Conference on Machine Translation, pages 979–984, Online. Association for Computational Linguistics.