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        <title>UDAPTER - Efficient Domain Adaptation Using Adapters</title>
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    <name type="personal">
        <namePart type="given">Bhavitvya</namePart>
        <namePart type="family">Malik</namePart>
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    <name type="personal">
        <namePart type="given">Abhinav</namePart>
        <namePart type="family">Ramesh Kashyap</namePart>
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    <name type="personal">
        <namePart type="given">Min-Yen</namePart>
        <namePart type="family">Kan</namePart>
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    <name type="personal">
        <namePart type="given">Soujanya</namePart>
        <namePart type="family">Poria</namePart>
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        <dateIssued>2023-05</dateIssued>
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            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>We propose two methods to make unsupervised domain adaptation (UDA) more parameter efficient using adapters – small bottleneck layers interspersed with every layer of the large-scale pre-trained language model (PLM). The first method deconstructs UDA into a two-step process: first by adding a domain adapter to learn domain-invariant information and then by adding a task adapter that uses domain-invariant information to learn task representations in the source domain. The second method jointly learns a supervised classifier while reducing the divergence measure. Compared to strong baselines, our simple methods perform well in natural language inference (MNLI) and the cross-domain sentiment classification task. We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0.85% F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters. We release our code at this URL.</abstract>
    <identifier type="citekey">malik-etal-2023-udapter</identifier>
    <identifier type="doi">10.18653/v1/2023.eacl-main.165</identifier>
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        <url>https://aclanthology.org/2023.eacl-main.165</url>
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        <date>2023-05</date>
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            <start>2249</start>
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%0 Conference Proceedings
%T UDAPTER - Efficient Domain Adaptation Using Adapters
%A Malik, Bhavitvya
%A Ramesh Kashyap, Abhinav
%A Kan, Min-Yen
%A Poria, Soujanya
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F malik-etal-2023-udapter
%X We propose two methods to make unsupervised domain adaptation (UDA) more parameter efficient using adapters – small bottleneck layers interspersed with every layer of the large-scale pre-trained language model (PLM). The first method deconstructs UDA into a two-step process: first by adding a domain adapter to learn domain-invariant information and then by adding a task adapter that uses domain-invariant information to learn task representations in the source domain. The second method jointly learns a supervised classifier while reducing the divergence measure. Compared to strong baselines, our simple methods perform well in natural language inference (MNLI) and the cross-domain sentiment classification task. We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0.85% F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters. We release our code at this URL.
%R 10.18653/v1/2023.eacl-main.165
%U https://aclanthology.org/2023.eacl-main.165
%U https://doi.org/10.18653/v1/2023.eacl-main.165
%P 2249-2263

Markdown (Informal)

[UDAPTER - Efficient Domain Adaptation Using Adapters](https://aclanthology.org/2023.eacl-main.165) (Malik et al., EACL 2023)

• UDAPTER - Efficient Domain Adaptation Using Adapters (Malik et al., EACL 2023)

ACL

• Bhavitvya Malik, Abhinav Ramesh Kashyap, Min-Yen Kan, and Soujanya Poria. 2023. UDAPTER - Efficient Domain Adaptation Using Adapters. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2249–2263, Dubrovnik, Croatia. Association for Computational Linguistics.