论文标题
迈向改进的作者身份模型设计：关于写作风格理解的调查
Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding
论文作者
论文摘要
在很大程度上依赖语言风格的作者身份识别任务一直是自然语言理解（NLU）研究的重要组成部分。尽管基于语言风格的其他任务从深度学习方法中受益，但这些方法在许多基于作者的任务中都没有表现得不如传统的机器学习方法。但是，随着这些任务变得越来越具有挑战性，基于手工特征集的传统机器学习方法已经接近其性能限制。因此，为了激发基于作者的任务中深度学习方法的未来应用，以有利于风格特征的提取的方式，我们调查了基于作者的任务以及与写作样式理解有关的其他任务。我们首先描述了有关两组任务中研究状态的调查结果，并总结了与作者相关的任务中的现有成就和问题。然后，我们一般描述了与样式相关任务的出色方法，并分析了它们在表现最佳模型中的组合使用方式。我们对这些模型对基于作者的任务的适用性感到乐观，并希望我们的调查将有助于推进该领域的研究。
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.
