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        <title>Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning</title>
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    <abstract>A shift in data distribution can have a significant impact on performance of a text classification model. Recent methods addressing unsupervised domain adaptation for textual tasks typically extracted domain-invariant representations through balancing between multiple objectives to align feature spaces between source and target domains. While effective, these methods induce various new domain-sensitive hyperparameters, thus are impractical as large-scale language models are drastically growing bigger to achieve optimal performance. To this end, we propose to leverage meta-learning framework to train a neural network-based self-paced learning procedure in an end-to-end manner. Our method, called Meta Self-Paced Domain Adaption (MSP-DA), follows a novel but intuitive domain-shift variation of cluster assumption to derive the meta train-test dataset split based on the self-pacing difficulties of source domain’s examples. As a result, MSP-DA effectively leverages self-training and self-tuning domain-specific hyperparameters simultaneously throughout the learning process. Extensive experiments demonstrate our framework substantially improves performance on target domains, surpassing state-of-the-art approaches. Detailed analyses validate our method and provide insight into how each domain affects the learned hyperparameters.</abstract>
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%0 Conference Proceedings
%T Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning
%A Trung, Nghia Ngo
%A Van, Linh Ngo
%A Nguyen, Thien Huu
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F trung-etal-2022-unsupervised
%X A shift in data distribution can have a significant impact on performance of a text classification model. Recent methods addressing unsupervised domain adaptation for textual tasks typically extracted domain-invariant representations through balancing between multiple objectives to align feature spaces between source and target domains. While effective, these methods induce various new domain-sensitive hyperparameters, thus are impractical as large-scale language models are drastically growing bigger to achieve optimal performance. To this end, we propose to leverage meta-learning framework to train a neural network-based self-paced learning procedure in an end-to-end manner. Our method, called Meta Self-Paced Domain Adaption (MSP-DA), follows a novel but intuitive domain-shift variation of cluster assumption to derive the meta train-test dataset split based on the self-pacing difficulties of source domain’s examples. As a result, MSP-DA effectively leverages self-training and self-tuning domain-specific hyperparameters simultaneously throughout the learning process. Extensive experiments demonstrate our framework substantially improves performance on target domains, surpassing state-of-the-art approaches. Detailed analyses validate our method and provide insight into how each domain affects the learned hyperparameters.
%U https://aclanthology.org/2022.coling-1.420
%P 4741-4752

Markdown (Informal)

[Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning](https://aclanthology.org/2022.coling-1.420) (Trung et al., COLING 2022)

• Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning (Trung et al., COLING 2022)

ACL

• Nghia Ngo Trung, Linh Ngo Van, and Thien Huu Nguyen. 2022. Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4741–4752, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.