Argument Mining with Structured SVMs and RNNs

April 23, 2017 · Entered Twilight · 🏛 Annual Meeting of the Association for Computational Linguistics

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Authors Vlad Niculae, Joonsuk Park, Claire Cardie arXiv ID 1704.06869 Category cs.CL: Computation & Language Citations 116 Venue Annual Meeting of the Association for Computational Linguistics Repository https://github.com/vene/marseille ⭐ 67 Last Checked 1 month ago

Abstract

We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.