@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",
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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<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.</abstract>
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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
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%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)
ACL