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        <title>Data Augmentation via Dependency Tree Morphing for Low-Resource Languages</title>
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        <namePart type="given">Gözde</namePart>
        <namePart type="given">Gül</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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    <abstract>Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We “crop” sentences by removing dependency links, and we “rotate” sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1545</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
%A Şahin, Gözde Gül
%A Steedman, Mark
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F sahin-steedman-2018-data
%X Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We “crop” sentences by removing dependency links, and we “rotate” sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.
%R 10.18653/v1/D18-1545
%U https://aclanthology.org/D18-1545
%U https://doi.org/10.18653/v1/D18-1545
%P 5004-5009

Markdown (Informal)

[Data Augmentation via Dependency Tree Morphing for Low-Resource Languages](https://aclanthology.org/D18-1545) (Şahin & Steedman, EMNLP 2018)

• Data Augmentation via Dependency Tree Morphing for Low-Resource Languages (Şahin & Steedman, EMNLP 2018)

ACL

• Gözde Gül Şahin and Mark Steedman. 2018. Data Augmentation via Dependency Tree Morphing for Low-Resource Languages. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 5004–5009, Brussels, Belgium. Association for Computational Linguistics.