Modeling Argument Strength in Student Essays

Isaac Persing and Vincent Ng.
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 543-552, 2015.

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Abstract

While recent years have seen a surge of interest in automated essay grading, including work on grading essays with respect to particular dimensions such as prompt adherence, coherence, and technical quality, there has been relatively little work on grading the essay dimension of argument strength, which is arguably the most important aspect of argumentative essays. We introduce a new corpus of argumentative student essays annotated with argument strength scores and propose a supervised, feature-rich approach to automatically scoring the essays along this dimension. Our approach significantly outperforms a baseline that relies solely on heuristically applied sentence argument function labels by up to 16.1%.

Dataset

The human annotation used in this paper is available from this page.

BibTeX entry

@InProceedings{Persing+Ng:15a,
  author = {Isaac Persing and Vincent Ng},
  title = {Modeling Argument Strength in Student Essays},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  pages = {543--552},
  year = 2015}

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