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        <title>How Good Is the Model in Model-in-the-loop Event Coreference Resolution Annotation?</title>
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    <name type="personal">
        <namePart type="given">Shafiuddin</namePart>
        <namePart type="given">Rehan</namePart>
        <namePart type="family">Ahmed</namePart>
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        <namePart type="given">Abhijnan</namePart>
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        <namePart type="given">Adam</namePart>
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    <name type="personal">
        <namePart type="given">Nikhil</namePart>
        <namePart type="family">Krishnaswamy</namePart>
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            <title>Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)</title>
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            <namePart type="family">Prange</namePart>
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            <namePart type="given">Annemarie</namePart>
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    <abstract>Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering event pairs only. We evaluate the effectiveness of this approach by first simulating the annotation process and then, using a novel annotator-centric Recall-Annotation effort trade-off metric, we compare the results of various underlying models and datasets. We finally present a method for obtaining 97% recall while substantially reducing the workload required by a fully manual annotation process.</abstract>
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    <identifier type="doi">10.18653/v1/2023.law-1.14</identifier>
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%0 Conference Proceedings
%T How Good Is the Model in Model-in-the-loop Event Coreference Resolution Annotation?
%A Ahmed, Shafiuddin Rehan
%A Nath, Abhijnan
%A Regan, Michael
%A Pollins, Adam
%A Krishnaswamy, Nikhil
%A Martin, James H.
%Y Prange, Jakob
%Y Friedrich, Annemarie
%S Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ahmed-etal-2023-good
%X Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering event pairs only. We evaluate the effectiveness of this approach by first simulating the annotation process and then, using a novel annotator-centric Recall-Annotation effort trade-off metric, we compare the results of various underlying models and datasets. We finally present a method for obtaining 97% recall while substantially reducing the workload required by a fully manual annotation process.
%R 10.18653/v1/2023.law-1.14
%U https://aclanthology.org/2023.law-1.14
%U https://doi.org/10.18653/v1/2023.law-1.14
%P 136-145

Markdown (Informal)

[How Good Is the Model in Model-in-the-loop Event Coreference Resolution Annotation?](https://aclanthology.org/2023.law-1.14) (Ahmed et al., LAW 2023)

• How Good Is the Model in Model-in-the-loop Event Coreference Resolution Annotation? (Ahmed et al., LAW 2023)

ACL

• Shafiuddin Rehan Ahmed, Abhijnan Nath, Michael Regan, Adam Pollins, Nikhil Krishnaswamy, and James H. Martin. 2023. How Good Is the Model in Model-in-the-loop Event Coreference Resolution Annotation?. In Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII), pages 136–145, Toronto, Canada. Association for Computational Linguistics.