Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner

Abstract

Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an `Agree' and a `Disagree' network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the dataset-specific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book.

Keywords

• Authorship discrimination
• deep learning
• machine learning
• natural language processing
• neural networks