Scoring Persuasive Essays Using Opinions and their Targets

Farra, Noura; Somasundaran, Swapna; Burstein, Jill

doi:10.3115/v1/w15-0608

Cited by 45 publications

(50 citation statements)

References 24 publications

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“…Farra et al (2015) utilize variants of logistic and linear regression and develop models that score persuasive essays based on features extracted from opinion expressions and topical elements.…”

Section: Related Workmentioning

confidence: 99%

Automatic Text Scoring Using Neural Networks

Alikaniotis¹,

Yannakoudakis²,

Rei³

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

203

182

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Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking. However, in order to achieve good performance, the predictive features of the system need to be manually engineered by human experts. We introduce a model that forms word representations by learning the extent to which specific words contribute to the text's score. Using Long-Short Term Memory networks to represent the meaning of texts, we demonstrate that a fully automated framework is able to achieve excellent results over similar approaches. In an attempt to make our results more interpretable, and inspired by recent advances in visualizing neural networks, we introduce a novel method for identifying the regions of the text that the model has found more discriminative.

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Section: Related Workmentioning

confidence: 99%

Automatic Text Scoring Using Neural Networks

Alikaniotis¹,

Yannakoudakis²,

Rei³

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

203

182

View full text Add to dashboard Cite

show abstract

“…AES systems may rely not only on grammars, but also on more complex features such as semantics, discourse and pragmatics (Davis and Veloso, 2016;Song et al, 2014;Farra et al, 2015;Somasundaran et al, 2014). Thus, a prominent approach to AES is to learn scoring models from previously graded samples, by modeling the scoring process of human raters.…”

Section: Introductionmentioning

confidence: 99%

Automated Essay Scoring in the Presence of Biased Ratings

Amorim¹,

Cançado²,

Veloso³

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Studies in Social Sciences have revealed that when people evaluate someone else, their evaluations often reflect their biases. As a result, rater bias may introduce highly subjective factors that make their evaluations inaccurate. This may affect automated essay scoring models in many ways, as these models are typically designed to model (potentially biased) essay raters. While there is sizeable literature on rater effects in general settings, it remains unknown how rater bias affects automated essay scoring. To this end, we present a new annotated corpus containing essays and their respective scores. Different from existing corpora, our corpus also contains comments provided by the raters in order to ground their scores. We present features to quantify rater bias based on their comments, and we found that rater bias plays an important role in automated essay scoring. We investigated the extent to which rater bias affects models based on hand-crafted features. Finally, we propose to rectify the training set by removing essays associated with potentially biased scores while learning the scoring model.

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“…Farra et al (2015) investigate the impact of opinion and target features on TOEFL essays scores. Our work looks a step further by exploring argumentation features.…”

Section: Related Workmentioning

confidence: 99%

“…Researchers have begun to study the impact of features specific to persuasive construct on student essay scores (Farra et al, 2015;Ong et al, 2014;Persing and Ng, 2013;Persing and Ng, 2015). Farra et al (2015) investigate the impact of opinion and target features on TOEFL essays scores.…”

Section: Related Workmentioning

confidence: 99%

“…However, recent work has begun to explore the impact of highlevel persuasion-related features, such as opinions and their targets, thesis clarity and argumentation schemes (Farra et al, 2015;Ong et al, 2014;Persing and Ng, 2015). In this paper, we investigate whether argumentation features derived from a coarse-grained, general argumentative structure of essays are good predictors of holistic essay scores.…”

Section: Introductionmentioning

confidence: 99%

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