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Authorship identification tasks, which rely heavily on linguistic styles,have always been an important part of Natural Language Understanding (NLU)research. While other tasks based on linguistic style understanding benefitfrom deep learning methods, these methods have not behaved as well astraditional machine learning methods in many authorship-based tasks. With thesetasks becoming more and more challenging, however, traditional machine learningmethods based on handcrafted feature sets are already approaching theirperformance limits. Thus, in order to inspire future applications of deeplearning methods in authorship-based tasks in ways that benefit the extractionof stylistic features, we survey authorship-based tasks and other tasks relatedto writing style understanding. We first describe our survey results on thecurrent state of research in both sets of tasks and summarize existingachievements and problems in authorship-related tasks. We then describeoutstanding methods in style-related tasks in general and analyze how they areused in combination in the top-performing models. We are optimistic about theapplicability of these models to authorship-based tasks and hope our surveywill help advance research in this field.

Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding