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    <titleInfo>
        <title>Sister Help: Data Augmentation for Frame-Semantic Role Labeling</title>
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    <name type="personal">
        <namePart type="given">Ayush</namePart>
        <namePart type="family">Pancholy</namePart>
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        <namePart type="given">Miriam</namePart>
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        <namePart type="given">L</namePart>
        <namePart type="family">Petruck</namePart>
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    <name type="personal">
        <namePart type="given">Swabha</namePart>
        <namePart type="family">Swayamdipta</namePart>
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            <title>Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop</title>
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        <name type="personal">
            <namePart type="given">Claire</namePart>
            <namePart type="family">Bonial</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nianwen</namePart>
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    <abstract>While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data compared to other commonly used lexical resources such as PropBank and VerbNet. This paper reports on a pilot study to address these gaps. We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated. Our rule-based approach defines the notion of a **sister lexical unit** and generates frame-specific augmented data for training. We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation: we obtain a large improvement to prior results on frame identification and argument identification for FrameNet, utilizing both full-text and lexicographic annotations under FrameNet. Our findings on data augmentation highlight the value of automatic resource creation for improved models in frame-semantic parsing.</abstract>
    <identifier type="citekey">pancholy-etal-2021-sister</identifier>
    <identifier type="doi">10.18653/v1/2021.law-1.8</identifier>
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        <date>2021-11</date>
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            <start>78</start>
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%0 Conference Proceedings
%T Sister Help: Data Augmentation for Frame-Semantic Role Labeling
%A Pancholy, Ayush
%A Petruck, Miriam R. L.
%A Swayamdipta, Swabha
%Y Bonial, Claire
%Y Xue, Nianwen
%S Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F pancholy-etal-2021-sister
%X While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data compared to other commonly used lexical resources such as PropBank and VerbNet. This paper reports on a pilot study to address these gaps. We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated. Our rule-based approach defines the notion of a **sister lexical unit** and generates frame-specific augmented data for training. We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation: we obtain a large improvement to prior results on frame identification and argument identification for FrameNet, utilizing both full-text and lexicographic annotations under FrameNet. Our findings on data augmentation highlight the value of automatic resource creation for improved models in frame-semantic parsing.
%R 10.18653/v1/2021.law-1.8
%U https://aclanthology.org/2021.law-1.8
%U https://doi.org/10.18653/v1/2021.law-1.8
%P 78-84

Markdown (Informal)

[Sister Help: Data Augmentation for Frame-Semantic Role Labeling](https://aclanthology.org/2021.law-1.8) (Pancholy et al., LAW 2021)

• Sister Help: Data Augmentation for Frame-Semantic Role Labeling (Pancholy et al., LAW 2021)

ACL

• Ayush Pancholy, Miriam R L Petruck, and Swabha Swayamdipta. 2021. Sister Help: Data Augmentation for Frame-Semantic Role Labeling. In Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 78–84, Punta Cana, Dominican Republic. Association for Computational Linguistics.