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        <title>Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages</title>
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        <namePart type="given">Mathieu</namePart>
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    <name type="personal">
        <namePart type="given">Carlos</namePart>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>The lack of annotated data is a big issue for building reliable NLP systems for most of the world’s languages. But this problem can be alleviated by automatic data generation. In this paper, we present a new data augmentation method for artificially creating new dependency-annotated sentences. The main idea is to swap subtrees between annotated sentences while enforcing strong constraints on those trees to ensure maximal grammaticality of the new sentences. We also propose a method to perform low-resource experiments using resource-rich languages by mimicking low-resource languages by sampling sentences under a low-resource distribution. In a series of experiments, we show that our newly proposed data augmentation method outperforms previous proposals using the same basic inputs.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.339</identifier>
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%0 Conference Proceedings
%T Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages
%A Dehouck, Mathieu
%A Gómez-Rodríguez, Carlos
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F dehouck-gomez-rodriguez-2020-data
%X The lack of annotated data is a big issue for building reliable NLP systems for most of the world’s languages. But this problem can be alleviated by automatic data generation. In this paper, we present a new data augmentation method for artificially creating new dependency-annotated sentences. The main idea is to swap subtrees between annotated sentences while enforcing strong constraints on those trees to ensure maximal grammaticality of the new sentences. We also propose a method to perform low-resource experiments using resource-rich languages by mimicking low-resource languages by sampling sentences under a low-resource distribution. In a series of experiments, we show that our newly proposed data augmentation method outperforms previous proposals using the same basic inputs.
%R 10.18653/v1/2020.coling-main.339
%U https://aclanthology.org/2020.coling-main.339
%U https://doi.org/10.18653/v1/2020.coling-main.339
%P 3818-3830

Markdown (Informal)

[Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages](https://aclanthology.org/2020.coling-main.339) (Dehouck & Gómez-Rodríguez, COLING 2020)

• Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages (Dehouck & Gómez-Rodríguez, COLING 2020)

ACL

• Mathieu Dehouck and Carlos Gómez-Rodríguez. 2020. Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3818–3830, Barcelona, Spain (Online). International Committee on Computational Linguistics.