Applying Rhetorical Structure Theory to Student Essays for Providing Automated Writing Feedback

Jiang, Shiyan; Yang, Kexin; Suvarna, Chandrakumari; Casula, Pooja; Zhang, Mingtong; Rosé, Carolyn P.

doi:10.18653/v1/w19-2720

Cited by 10 publications

(11 citation statements)

References 11 publications

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“…We also consider an RST corpus of less-polished student writing (Jiang et al, 2019). The corpus consists of 274 essays collected from Turnitin Revision Assistant (West-Smith et al, 2018), responding to standards-aligned formative writing tasks (Valencia and Wixson, 2001).…”

Section: Applying Rhetorical Structure Theory To Student Writingmentioning

confidence: 99%

“…Efforts have been made to address the challenge of providing structural level feedback via automatically extracting discourse structure from essays (Burstein et al, 2003). Extracting hierarchical discourse structure and organization from documents has been shown to be valuable for numerous applications including text categorization, authorship attribution, and automatic essay feedback (Feng and Hirst, 2014b;Jiang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation

Fiacco¹,

Jiang²,

Adamson³

et al. 2022

Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

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Providing effective automatic essay feedback is necessary for offering writing instruction at a massive scale. In particular, feedback for promoting coherent flow of ideas in essays is critical. In this paper we propose a state-of-the-art method for automated analysis of structure and flow of writing, referred to as Rhetorical Structure Theory (RST) parsing. In so doing, we lay a foundation for a generalizable approach to automated writing feedback related to structure and flow. We address challenges in automated rhetorical analysis when applied to student writing and evaluate our novel RST parser model on both a recent student writing dataset and a standard benchmark RST parsing dataset.

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Section: Applying Rhetorical Structure Theory To Student Writingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation

Fiacco¹,

Jiang²,

Adamson³

et al. 2022

Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

Self Cite

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“…Analysis of discourse structure There has been a lot effort to understand the role of discourse structure in argumentation. Jiang et al (2019) applied RST to essays written by students in K-12 schools and demonstrated its potential to provide automated feedback for essay quality. Argument structures, which can be considered as a special kind of discourse structure, have been widely analyzed in the task of automatic essay scoring and feedback (Klebanov et al, 2016;Ghosh et al, 2016;Wachsmuth et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Exploring the Role of Argument Structure in Online Debate Persuasion

Durmus

Cardie

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments. We find that incorporating argument structure features play an essential role in achieving the better predictive performance in assessing the persuasiveness of the arguments in online debates.

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“…Jiang, Yang, Suvarna, Cassula, Zhang and Rose (Jiang et al, 2019) present a package of annotation resources that can be used to apply RST to essays written by students. Furthermore, they highlight the great potential of using RST to provide automated feedback for improving writing quality across genres.…”

Section: Workhop Papersmentioning

confidence: 99%

Introduction to Discourse Relation Parsing and Treebanking (

Zeldes¹,

Das²,

Maziero³

et al. 2019

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop's aim was to bring together researchers working on corpus-based and computational approaches to discourse relations. In addition to an invited talk, eighteen papers outlined below were presented, four of which were submitted as part of a shared task on elementary discourse unit segmentation and connective detection.

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Applying Rhetorical Structure Theory to Student Essays for Providing Automated Writing Feedback

Cited by 10 publications

References 11 publications

Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation

Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation

Exploring the Role of Argument Structure in Online Debate Persuasion

Introduction to Discourse Relation Parsing and Treebanking (

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