End-to-End Argumentation Mining in Student Essays

Isaac Persing and Vincent Ng.
Proceedings of Human Language Technologies: The 2016 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 1384-1394, 2016.

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Abstract

Understanding the argumentative structure of a persuasive essay involves addressing two challenging tasks: identifying the components of the essay's argument and identifying the relations that occur between them. We examine the under-investigated task of end-to-end argument mining in persuasive student essays, where we (1) present the first results on end-to-end argument mining in student essays using a pipeline approach; (2) address error propagation inherent in the pipeline approach by performing joint inference over the outputs of the tasks in an Integer Linear Programming (ILP) framework; and (3) propose a novel objective function that enables F-score to be maximized directly by an ILP solver. We evaluate our joint-inference approach with our novel objective function on a publicly-available corpus of 90 essays, where it yields an 18.5% relative error reduction in F-score over the pipeline system.

BibTeX entry

@InProceedings{Persing+Ng:16a,
  author = {Isaac Persing and Vincent Ng},
  title = {End-to-End Argumentation Mining in Student Essays},
  booktitle = {Proceedings of Human Language Technologies: The 2016 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
  pages = {1384--1394},
  year = 2016}