Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers. (arXiv:2205.05435v5 [cs.CL] UPDATED)

Performance of text classification models tends to drop over time due to
changes in data, which limits the lifetime of a pretrained model. Therefore an
ability to predict a model's ability to persist over time can help design
models that can be effectively used over a longer period of time. In this
paper, we look at this problem from a practical perspective by assessing the
ability of a wide range of language models and classification algorithms to
persist over time, …

arxiv building classifiers persistence temporal text