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        <title>Semi-Supervised Domain Adaptation for Emotion-Related Tasks</title>
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    <abstract>Semi-supervised domain adaptation (SSDA) adopts a model trained from a label-rich source domain to a new but related domain with a few labels of target data. It is shown that, in an SSDA setting, a simple combination of domain adaptation (DA) with semi-supervised learning (SSL) techniques often fails to effectively utilize the target supervision and cannot address distribution shifts across different domains due to the training data bias toward the source-labeled samples. In this paper, inspired by the co-learning of multiple classifiers for the computer vision tasks, we propose to decompose the SSDA framework for emotion-related tasks into two subcomponents of unsupervised domain adaptation (UDA) from the source to the target domain and semi-supervised learning (SSL) in the target domain where the two models iteratively teach each other by interchanging their high confident predictions. We further propose a novel data cartography-based regularization technique for pseudo-label denoising that employs training dynamics to further hone our models’ performance. We publicly release our code.</abstract>
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%0 Conference Proceedings
%T Semi-Supervised Domain Adaptation for Emotion-Related Tasks
%A Hosseini, Mahshid
%A Caragea, Cornelia
%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 hosseini-caragea-2023-semi
%X Semi-supervised domain adaptation (SSDA) adopts a model trained from a label-rich source domain to a new but related domain with a few labels of target data. It is shown that, in an SSDA setting, a simple combination of domain adaptation (DA) with semi-supervised learning (SSL) techniques often fails to effectively utilize the target supervision and cannot address distribution shifts across different domains due to the training data bias toward the source-labeled samples. In this paper, inspired by the co-learning of multiple classifiers for the computer vision tasks, we propose to decompose the SSDA framework for emotion-related tasks into two subcomponents of unsupervised domain adaptation (UDA) from the source to the target domain and semi-supervised learning (SSL) in the target domain where the two models iteratively teach each other by interchanging their high confident predictions. We further propose a novel data cartography-based regularization technique for pseudo-label denoising that employs training dynamics to further hone our models’ performance. We publicly release our code.
%R 10.18653/v1/2023.findings-acl.333
%U https://aclanthology.org/2023.findings-acl.333
%U https://doi.org/10.18653/v1/2023.findings-acl.333
%P 5402-5410

Markdown (Informal)

[Semi-Supervised Domain Adaptation for Emotion-Related Tasks](https://aclanthology.org/2023.findings-acl.333) (Hosseini & Caragea, Findings 2023)

• Semi-Supervised Domain Adaptation for Emotion-Related Tasks (Hosseini & Caragea, Findings 2023)

ACL

• Mahshid Hosseini and Cornelia Caragea. 2023. Semi-Supervised Domain Adaptation for Emotion-Related Tasks. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5402–5410, Toronto, Canada. Association for Computational Linguistics.