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    <titleInfo>
        <title>On Dataset Transferability in Active Learning for Transformers</title>
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    <name type="personal">
        <namePart type="given">Fran</namePart>
        <namePart type="family">Jelenić</namePart>
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    <name type="personal">
        <namePart type="given">Josip</namePart>
        <namePart type="family">Jukić</namePart>
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    <name type="personal">
        <namePart type="given">Nina</namePart>
        <namePart type="family">Drobac</namePart>
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    <name type="personal">
        <namePart type="given">Jan</namePart>
        <namePart type="family">Snajder</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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            <title>Findings of the Association for Computational Linguistics: ACL 2023</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
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        <name type="personal">
            <namePart type="given">Jordan</namePart>
            <namePart type="family">Boyd-Graber</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Naoaki</namePart>
            <namePart type="family">Okazaki</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Active learning (AL) aims to reduce labeling costs by querying the examples most beneficial for model learning. While the effectiveness of AL for fine-tuning transformer-based pre-trained language models (PLMs) has been demonstrated, it is less clear to what extent the AL gains obtained with one model transfer to others. We consider the problem of transferability of actively acquired datasets in text classification and investigate whether AL gains persist when a dataset built using AL coupled with a specific PLM is used to train a different PLM. We link the AL dataset transferability to the similarity of instances queried by the different PLMs and show that AL methods with similar acquisition sequences produce highly transferable datasets regardless of the models used. Additionally, we show that the similarity of acquisition sequences is influenced more by the choice of the AL method than the choice of the model.</abstract>
    <identifier type="citekey">jelenic-etal-2023-dataset</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-acl.144</identifier>
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        <url>https://aclanthology.org/2023.findings-acl.144</url>
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        <date>2023-07</date>
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            <start>2282</start>
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%0 Conference Proceedings
%T On Dataset Transferability in Active Learning for Transformers
%A Jelenić, Fran
%A Jukić, Josip
%A Drobac, Nina
%A Snajder, Jan
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F jelenic-etal-2023-dataset
%X Active learning (AL) aims to reduce labeling costs by querying the examples most beneficial for model learning. While the effectiveness of AL for fine-tuning transformer-based pre-trained language models (PLMs) has been demonstrated, it is less clear to what extent the AL gains obtained with one model transfer to others. We consider the problem of transferability of actively acquired datasets in text classification and investigate whether AL gains persist when a dataset built using AL coupled with a specific PLM is used to train a different PLM. We link the AL dataset transferability to the similarity of instances queried by the different PLMs and show that AL methods with similar acquisition sequences produce highly transferable datasets regardless of the models used. Additionally, we show that the similarity of acquisition sequences is influenced more by the choice of the AL method than the choice of the model.
%R 10.18653/v1/2023.findings-acl.144
%U https://aclanthology.org/2023.findings-acl.144
%U https://doi.org/10.18653/v1/2023.findings-acl.144
%P 2282-2295

Markdown (Informal)

[On Dataset Transferability in Active Learning for Transformers](https://aclanthology.org/2023.findings-acl.144) (Jelenić et al., Findings 2023)

• On Dataset Transferability in Active Learning for Transformers (Jelenić et al., Findings 2023)

ACL

• Fran Jelenić, Josip Jukić, Nina Drobac, and Jan Snajder. 2023. On Dataset Transferability in Active Learning for Transformers. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2282–2295, Toronto, Canada. Association for Computational Linguistics.