Towards Enabling Feedback on Rhetorical Structure with Neural Sequence Models

Fiacco, James; Cotos, Elena; Rosé, Carolyn Penstein

doi:10.1145/3303772.3303808

Cited by 19 publications

(15 citation statements)

References 27 publications

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“…Algorithmic assessment of student writing has taken many forms, from summative use in standardized testing (Shermis and Hamner, 2012) and the GRE (Chen et al, 2016) to formative use for classroom feedback (Woods et al, 2017;Wilson and Roscoe, 2019). This trend has led to sophisticated NLP analyses like argument mining (Nguyen and Litman, 2018) and rhetorical structure detection (Fiacco et al, 2019). Automated scoring has seen some more limited use in higher education, as well (Cotos, 2014;Johnson et al, 2017).…”

Section: Case Study: Automated Writing Feedback and Scoringmentioning

confidence: 99%

Equity Beyond Bias in Language Technologies for Education

Mayfield¹,

Madaio²,

Prabhumoye³

et al. 2019

Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

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There is a long record of research on equity in schools. As machine learning researchers begin to study fairness and bias in earnest, language technologies in education have an unusually strong theoretical and applied foundation to build on. Here, we introduce concepts from culturally relevant pedagogy and other frameworks for teaching and learning, and identify future work on equity in NLP. We present case studies in a range of topics like intelligent tutoring systems, computer-assisted language learning, automated essay scoring, and sentiment analysis in classrooms, and provide an actionable agenda for research.

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Section: Case Study: Automated Writing Feedback and Scoringmentioning

confidence: 99%

Equity Beyond Bias in Language Technologies for Education

Mayfield¹,

Madaio²,

Prabhumoye³

et al. 2019

Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

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“…Currently, based on the work from Li et al (2014), we are building an RST parser that can generate RST trees to represent student essays automatically with deep learning techniques. In the future, we plan to build the work from Fiacco et al (2019) to generate RST trees more accurately and efficiently. Our long term goal is to embed these techniques in a writing tutor like Revision Assistant and conduct large-scale classroom studies to evaluate the effect of RST trees in writing instruction.…”

Section: Resultsmentioning

confidence: 99%

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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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Section: Discussionmentioning

confidence: 99%

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

Jiang¹,

Yang²,

Suvarna³

et al. 2019

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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We present a package of annotation resources, including annotation guideline, flowchart, and an Intelligent Tutoring System for training human annotators. These resources can be used to apply Rhetorical Structure Theory (RST) to essays written by students in K-12 schools. Furthermore, we highlight the great potential of using RST to provide automated feedback for improving writing quality across genres.

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Towards Enabling Feedback on Rhetorical Structure with Neural Sequence Models

Cited by 19 publications

References 27 publications

Equity Beyond Bias in Language Technologies for Education

Equity Beyond Bias in Language Technologies for Education

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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

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