@inproceedings{liu-etal-2023-predicting,
title = "Predicting the Quality of Revisions in Argumentative Writing",
author = "Liu, Zhexiong and
Litman, Diane and
Wang, Elaine and
Matsumura, Lindsay and
Correnti, Richard",
editor = {Kochmar, Ekaterina and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Madnani, Nitin and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng and
Zesch, Torsten},
booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bea-1.24",
doi = "10.18653/v1/2023.bea-1.24",
pages = "275--287",
abstract = "The ability to revise in response to feedback is critical to students{'} writing success. In the case of argument writing in specific, identifying whether an argument revision (AR) is successful or not is a complex problem because AR quality is dependent on the overall content of an argument. For example, adding the same evidence sentence could strengthen or weaken existing claims in different argument contexts (ACs). To address this issue we developed Chain-of-Thought prompts to facilitate ChatGPT-generated ACs for AR quality predictions. The experiments on two corpora, our annotated elementary essays and existing college essays benchmark, demonstrate the superiority of the proposed ACs over baselines.",
}
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<abstract>The ability to revise in response to feedback is critical to students’ writing success. In the case of argument writing in specific, identifying whether an argument revision (AR) is successful or not is a complex problem because AR quality is dependent on the overall content of an argument. For example, adding the same evidence sentence could strengthen or weaken existing claims in different argument contexts (ACs). To address this issue we developed Chain-of-Thought prompts to facilitate ChatGPT-generated ACs for AR quality predictions. The experiments on two corpora, our annotated elementary essays and existing college essays benchmark, demonstrate the superiority of the proposed ACs over baselines.</abstract>
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%0 Conference Proceedings
%T Predicting the Quality of Revisions in Argumentative Writing
%A Liu, Zhexiong
%A Litman, Diane
%A Wang, Elaine
%A Matsumura, Lindsay
%A Correnti, Richard
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liu-etal-2023-predicting
%X The ability to revise in response to feedback is critical to students’ writing success. In the case of argument writing in specific, identifying whether an argument revision (AR) is successful or not is a complex problem because AR quality is dependent on the overall content of an argument. For example, adding the same evidence sentence could strengthen or weaken existing claims in different argument contexts (ACs). To address this issue we developed Chain-of-Thought prompts to facilitate ChatGPT-generated ACs for AR quality predictions. The experiments on two corpora, our annotated elementary essays and existing college essays benchmark, demonstrate the superiority of the proposed ACs over baselines.
%R 10.18653/v1/2023.bea-1.24
%U https://aclanthology.org/2023.bea-1.24
%U https://doi.org/10.18653/v1/2023.bea-1.24
%P 275-287
Markdown (Informal)
[Predicting the Quality of Revisions in Argumentative Writing](https://aclanthology.org/2023.bea-1.24) (Liu et al., BEA 2023)
ACL
- Zhexiong Liu, Diane Litman, Elaine Wang, Lindsay Matsumura, and Richard Correnti. 2023. Predicting the Quality of Revisions in Argumentative Writing. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 275–287, Toronto, Canada. Association for Computational Linguistics.