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        <title>Parallel Corpus Filtering via Pre-trained Language Models</title>
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        <namePart type="given">Boliang</namePart>
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        <namePart type="given">Ajay</namePart>
        <namePart type="family">Nagesh</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Web-crawled data provides a good source of parallel corpora for training machine translation models. It is automatically obtained, but extremely noisy, and recent work shows that neural machine translation systems are more sensitive to noise than traditional statistical machine translation methods. In this paper, we propose a novel approach to filter out noisy sentence pairs from web-crawled corpora via pre-trained language models. We measure sentence parallelism by leveraging the multilingual capability of BERT and use the Generative Pre-training (GPT) language model as a domain filter to balance data domains. We evaluate the proposed method on the WMT 2018 Parallel Corpus Filtering shared task, and on our own web-crawled Japanese-Chinese parallel corpus. Our method significantly outperforms baselines and achieves a new state-of-the-art. In an unsupervised setting, our method achieves comparable performance to the top-1 supervised method. We also evaluate on a web-crawled Japanese-Chinese parallel corpus that we make publicly available.</abstract>
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%0 Conference Proceedings
%T Parallel Corpus Filtering via Pre-trained Language Models
%A Zhang, Boliang
%A Nagesh, Ajay
%A Knight, Kevin
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-parallel-corpus
%X Web-crawled data provides a good source of parallel corpora for training machine translation models. It is automatically obtained, but extremely noisy, and recent work shows that neural machine translation systems are more sensitive to noise than traditional statistical machine translation methods. In this paper, we propose a novel approach to filter out noisy sentence pairs from web-crawled corpora via pre-trained language models. We measure sentence parallelism by leveraging the multilingual capability of BERT and use the Generative Pre-training (GPT) language model as a domain filter to balance data domains. We evaluate the proposed method on the WMT 2018 Parallel Corpus Filtering shared task, and on our own web-crawled Japanese-Chinese parallel corpus. Our method significantly outperforms baselines and achieves a new state-of-the-art. In an unsupervised setting, our method achieves comparable performance to the top-1 supervised method. We also evaluate on a web-crawled Japanese-Chinese parallel corpus that we make publicly available.
%R 10.18653/v1/2020.acl-main.756
%U https://aclanthology.org/2020.acl-main.756
%U https://doi.org/10.18653/v1/2020.acl-main.756
%P 8545-8554

Markdown (Informal)

[Parallel Corpus Filtering via Pre-trained Language Models](https://aclanthology.org/2020.acl-main.756) (Zhang et al., ACL 2020)

• Parallel Corpus Filtering via Pre-trained Language Models (Zhang et al., ACL 2020)

ACL

• Boliang Zhang, Ajay Nagesh, and Kevin Knight. 2020. Parallel Corpus Filtering via Pre-trained Language Models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8545–8554, Online. Association for Computational Linguistics.