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        <title>Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers</title>
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        <namePart type="given">Yousef</namePart>
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    <name type="personal">
        <namePart type="given">Hiroshi</namePart>
        <namePart type="family">Kanayama</namePart>
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    <name type="personal">
        <namePart type="given">Efsun</namePart>
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    <name type="personal">
        <namePart type="given">Vittorio</namePart>
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    <name type="personal">
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            <title>Proceedings of the 28th International Conference on Computational Linguistics: Industry Track</title>
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            <namePart type="given">Ann</namePart>
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    <abstract>We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text. We introduce a data augmentation technique that uses synthetic treebanks to improve production-grade parsers. The synthetic treebanks are generated using a state-of-the-art biaffine parser adapted with pretrained Transformer models, such as Multilingual BERT (M-BERT). The new parser improves LAS by up to two points on seven languages. The production models’ LAS performance improves as the augmented treebanks scale in size, surpassing performance of production models trained on originally annotated UD treebanks.</abstract>
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%0 Conference Proceedings
%T Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers
%A El-Kurdi, Yousef
%A Kanayama, Hiroshi
%A Sarioglu Kayi, Efsun
%A Castelli, Vittorio
%A Ward, Todd
%A Florian, Radu
%Y Clifton, Ann
%Y Napoles, Courtney
%S Proceedings of the 28th International Conference on Computational Linguistics: Industry Track
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Online
%F el-kurdi-etal-2020-scalable
%X We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text. We introduce a data augmentation technique that uses synthetic treebanks to improve production-grade parsers. The synthetic treebanks are generated using a state-of-the-art biaffine parser adapted with pretrained Transformer models, such as Multilingual BERT (M-BERT). The new parser improves LAS by up to two points on seven languages. The production models’ LAS performance improves as the augmented treebanks scale in size, surpassing performance of production models trained on originally annotated UD treebanks.
%R 10.18653/v1/2020.coling-industry.16
%U https://aclanthology.org/2020.coling-industry.16/
%U https://doi.org/10.18653/v1/2020.coling-industry.16
%P 172-178

Markdown (Informal)

[Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers](https://aclanthology.org/2020.coling-industry.16/) (El-Kurdi et al., COLING 2020)

• Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers (El-Kurdi et al., COLING 2020)

ACL

• Yousef El-Kurdi, Hiroshi Kanayama, Efsun Sarioglu Kayi, Vittorio Castelli, Todd Ward, and Radu Florian. 2020. Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers. In Proceedings of the 28th International Conference on Computational Linguistics: Industry Track, pages 172–178, Online. International Committee on Computational Linguistics.