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Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding
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 benefit
from deep learning methods, these methods have not behaved as well as
traditional machine learning methods in many authorship-based tasks. With these
tasks becoming more and more challenging, however, traditional machine learning
methods based on handcrafted feature sets are already approaching their
performance limits. Thus, in order to inspire future applications of deep
learning methods in authorship-based tasks in ways that benefit the extraction
of stylistic features, we survey authorship-based tasks and other tasks related
to writing style understanding. We first describe our survey results on the
current state of research in both sets of tasks and summarize existing
achievements and problems in authorship-related tasks. We then describe
outstanding methods in style-related tasks in general and analyze how they are
used in combination in the top-performing models. We are optimistic about the
applicability of these models to authorship-based tasks and hope our survey
will help advance research in this field