Authorship Attribution Using a Neural Network Language Model

Ge, Zhenhao; Sun, Yufang; Smith, M.J.T.

doi:10.48550/arxiv.1602.05292

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…We compare our approach with several state of the art baseline methods, including SVM, LDA-H (Seroussi, Zukerman, (Seroussi, Bohnert, and Zukerman 2012), TFS (Azarbonyad et al 2015) and NNLM (Ge, Sun, and Smith 2016).…”

Section: Baseline Methodsmentioning

confidence: 99%

Authorship Attribution with Topic Drift Model

Yang

Zhu

Tang

et al. 2017

AAAI

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Authorship attribution is an active research direction due to its legal and financial importance. The goal is to identify the authorship of anonymous texts. In this paper, we propose a Topic Drift Model (TDM), monitoring the dynamicity of authors’ writing style and latent topics of interest. Our model is sensitive to the temporal information and the ordering of words, thus it extracts more information from texts.

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Section: Baseline Methodsmentioning

confidence: 99%

Authorship Attribution with Topic Drift Model

Yang

Zhu

Tang

et al. 2017

AAAI

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Multi-Task Learning for Authorship Attribution via Topic Approximation and Competitive Attention

2019

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Separating content from style is a fundamental problem in authorship attribution to represent topic independent personal style of authors. Previous work often ignores this problem by imposing strong but unrealistic assumptions or artificially determines a set of predefined stylistic structures. This paper proposes to separate topic and style based on neural multi-task learning. Our target is to learn separate representations for topic and style respectively. In addition to authorship attribution as the main task, we introduce a novel auxiliary task topic approximation to guide the learning of topic representations with the topic distributions inferred by topic models, which are trained from external corpus. Moreover, we propose a competitive attention mechanism and a separation-reconstruction constraint to assign different and competitive attentions to two tasks in order to separate topic and style as much as possible. Evaluation results demonstrate that the proposed multi-task learning based method is promising, especially on crosstopic settings. We found that topic approximation can help capture topical content and the competitive attentions benefit topic-style separation. It is encouraging since our model separates topic and style in a probabilistic way and doesn't require human intervention.

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TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation

Uchendu¹,

Ma²,

Le³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Recent progress in generative language models has enabled machines to generate astonishingly realistic texts. While there are many legitimate applications of such models, there is also a rising need to distinguish machine-generated texts from human-written ones (e.g., fake news detection). However, to our best knowledge, there is currently no benchmark environment with datasets and tasks to systematically study the so-called "Turing Test" problem for neural text generation methods. In this work, we present the TURINGBENCH benchmark environment, which is comprised of (1) a dataset with 200K human-or machine-generated samples across 20 labels {Human,

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Authorship Attribution Using a Neural Network Language Model

Cited by 4 publications

References 3 publications

Authorship Attribution with Topic Drift Model

Authorship Attribution with Topic Drift Model

Multi-Task Learning for Authorship Attribution via Topic Approximation and Competitive Attention

TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation

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