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        <title>jp-evalb: Robust Alignment-based PARSEVAL Measures</title>
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        <namePart type="given">Jungyeul</namePart>
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    <name type="personal">
        <namePart type="given">Angela</namePart>
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            <title>Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)</title>
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            <namePart type="given">Kai-Wei</namePart>
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            <namePart type="family">Rajani</namePart>
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    <abstract>We introduce an evaluation system designed to compute PARSEVAL measures, offering a viable alternative to evalb commonly used for constituency parsing evaluation. The widely used evalb script has traditionally been employed for evaluating the accuracy of constituency parsing results, albeit with the requirement for consistent tokenization and sentence boundaries. In contrast, our approach, named jp-evalb, is founded on an alignment method. This method aligns sentences and words when discrepancies arise. It aims to overcome several known issues associated with evalb by utilizing the ‘jointly preprocessed (JP)’ alignment-based method. We introduce a more flexible and adaptive framework, ultimately contributing to a more accurate assessment of constituency parsing performance.</abstract>
    <identifier type="citekey">park-etal-2024-jp</identifier>
    <identifier type="doi">10.18653/v1/2024.naacl-demo.7</identifier>
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            <start>70</start>
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%0 Conference Proceedings
%T jp-evalb: Robust Alignment-based PARSEVAL Measures
%A Park, Jungyeul
%A Wang, Junrui
%A Jo, Eunkyul
%A Park, Angela
%Y Chang, Kai-Wei
%Y Lee, Annie
%Y Rajani, Nazneen
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F park-etal-2024-jp
%X We introduce an evaluation system designed to compute PARSEVAL measures, offering a viable alternative to evalb commonly used for constituency parsing evaluation. The widely used evalb script has traditionally been employed for evaluating the accuracy of constituency parsing results, albeit with the requirement for consistent tokenization and sentence boundaries. In contrast, our approach, named jp-evalb, is founded on an alignment method. This method aligns sentences and words when discrepancies arise. It aims to overcome several known issues associated with evalb by utilizing the ‘jointly preprocessed (JP)’ alignment-based method. We introduce a more flexible and adaptive framework, ultimately contributing to a more accurate assessment of constituency parsing performance.
%R 10.18653/v1/2024.naacl-demo.7
%U https://aclanthology.org/2024.naacl-demo.7
%U https://doi.org/10.18653/v1/2024.naacl-demo.7
%P 70-77

Markdown (Informal)

[jp-evalb: Robust Alignment-based PARSEVAL Measures](https://aclanthology.org/2024.naacl-demo.7) (Park et al., NAACL 2024)

• jp-evalb: Robust Alignment-based PARSEVAL Measures (Park et al., NAACL 2024)

ACL

• Jungyeul Park, Junrui Wang, Eunkyul Jo, and Angela Park. 2024. jp-evalb: Robust Alignment-based PARSEVAL Measures. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations), pages 70–77, Mexico City, Mexico. Association for Computational Linguistics.