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Automated text scoring (ATS) tasks, such as automated essay scoring andreadability assessment, are important educational applications of naturallanguage processing. Due to their interpretability of models and predictions,traditional machine learning (ML) algorithms based on handcrafted features arestill in wide use for ATS tasks. Practitioners often need to experiment with avariety of models (including deep and traditional ML ones), features, andtraining objectives (regression and classification), although modern deeplearning frameworks such as PyTorch require deep ML expertise to fully utilize.In this paper, we present EXPATS, an open-source framework to allow its usersto develop and experiment with different ATS models quickly by offeringflexible components, an easy-to-use configuration system, and the command-lineinterface. The toolkit also provides seamless integration with the LanguageInterpretability Tool (LIT) so that one can interpret and visualize models andtheir predictions. We also describe two case studies where we build ATS modelsquickly with minimal engineering efforts. The toolkit is available at\url{https://github.com/octanove/expats}.

EXPATS: A Toolkit for Explainable Automated Text Scoring