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    <titleInfo>
        <title>Data Augmentation Methods for Anaphoric Zero Pronouns</title>
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    <name type="personal">
        <namePart type="given">Abdulrahman</namePart>
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    <name type="personal">
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            <title>Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference</title>
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        <name type="personal">
            <namePart type="given">Maciej</namePart>
            <namePart type="family">Ogrodniczuk</namePart>
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            <namePart type="given">Sameer</namePart>
            <namePart type="family">Pradhan</namePart>
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    <abstract>In pro-drop language like Arabic, Chinese, Italian, Japanese, Spanish, and many others, unrealized (null) arguments in certain syntactic positions can refer to a previously introduced entity, and are thus called anaphoric zero pronouns. The existing resources for studying anaphoric zero pronoun interpretation are however still limited. In this paper, we use five data augmentation methods to generate and detect anaphoric zero pronouns automatically. We use the augmented data as additional training materials for two anaphoric zero pronoun systems for Arabic. Our experimental results show that data augmentation improves the performance of the two systems, surpassing the state-of-the-art results.</abstract>
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    <identifier type="doi">10.18653/v1/2021.crac-1.9</identifier>
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            <start>82</start>
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%0 Conference Proceedings
%T Data Augmentation Methods for Anaphoric Zero Pronouns
%A Aloraini, Abdulrahman
%A Poesio, Massimo
%Y Ogrodniczuk, Maciej
%Y Pradhan, Sameer
%Y Poesio, Massimo
%Y Grishina, Yulia
%Y Ng, Vincent
%S Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F aloraini-poesio-2021-data
%X In pro-drop language like Arabic, Chinese, Italian, Japanese, Spanish, and many others, unrealized (null) arguments in certain syntactic positions can refer to a previously introduced entity, and are thus called anaphoric zero pronouns. The existing resources for studying anaphoric zero pronoun interpretation are however still limited. In this paper, we use five data augmentation methods to generate and detect anaphoric zero pronouns automatically. We use the augmented data as additional training materials for two anaphoric zero pronoun systems for Arabic. Our experimental results show that data augmentation improves the performance of the two systems, surpassing the state-of-the-art results.
%R 10.18653/v1/2021.crac-1.9
%U https://aclanthology.org/2021.crac-1.9
%U https://doi.org/10.18653/v1/2021.crac-1.9
%P 82-93

Markdown (Informal)

[Data Augmentation Methods for Anaphoric Zero Pronouns](https://aclanthology.org/2021.crac-1.9) (Aloraini & Poesio, CRAC 2021)

• Data Augmentation Methods for Anaphoric Zero Pronouns (Aloraini & Poesio, CRAC 2021)

ACL

• Abdulrahman Aloraini and Massimo Poesio. 2021. Data Augmentation Methods for Anaphoric Zero Pronouns. In Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference, pages 82–93, Punta Cana, Dominican Republic. Association for Computational Linguistics.