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Adapting pre-trained language models (PLMs) for time-series textclassification amidst evolving domain shifts (EDS) is critical for maintainingaccuracy in applications like stance detection. This study benchmarks theeffectiveness of evolving domain adaptation (EDA) strategies, notablyself-training, domain-adversarial training, and domain-adaptive pretraining,with a focus on an incremental self-training method. Our analysis acrossvarious datasets reveals that this incremental method excels at adapting PLMsto EDS, outperforming traditional domain adaptation techniques. These findingshighlight the importance of continually updating PLMs to ensure theireffectiveness in real-world applications, paving the way for future researchinto PLM robustness against the natural temporal evolution of language.

Evolving Domain Adaptation of Pretrained Language Models for Text Classification