A Unified Representation and Deep Learning Architecture for Persuasive Essays in English

Muhammad Tawsif Sazid, The University of Western Ontario

Abstract

We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components—premises, claims, and major claims—and the argumentative relations—premise to claim or premise in a support or attack relation, and claim to major claim in a for or against relation—in an end-to-end machine learning pipeline. This tightly integrated representation combines the
component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtains state-of-the-art accuracy and F1 scores on the Persuasive Essays (PE) dataset. Furthermore, the augmentation of the PE corpus by including copies of major claims substituting the n-gram tokens that occur right before the major claim tokens with other major claim-introducing n-grams has aided in this increased performance on the PE dataset.