ICLE++: Modeling Fine-Grained Traits for Holistic Essay Scoring

Shengjie Li, Vincent Ng


Abstract
The majority of the recently developed models for automated essay scoring (AES) are evaluated solely on the ASAP corpus. However, ASAP is not without its limitations. For instance, it is not clear whether models trained on ASAP can generalize well when evaluated on other corpora. In light of these limitations, we introduce ICLE++, a corpus of persuasive student essays annotated with both holistic scores and trait-specific scores. Not only can ICLE++ be used to test the generalizability of AES models trained on ASAP, but it can also facilitate the evaluation of models developed for newer AES problems such as multi-trait scoring and cross-prompt scoring. We believe that ICLE++, which represents a culmination of our long-term effort in annotating the essays in the ICLE corpus, contributes to the set of much-needed annotated corpora for AES research.
Anthology ID:
2024.naacl-long.468
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
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Publisher:
Association for Computational Linguistics
Note:
Pages:
8458–8478
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URL:
https://aclanthology.org/2024.naacl-long.468
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Cite (ACL):
Shengjie Li and Vincent Ng. 2024. ICLE++: Modeling Fine-Grained Traits for Holistic Essay Scoring. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 8458–8478, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
ICLE++: Modeling Fine-Grained Traits for Holistic Essay Scoring (Li & Ng, NAACL 2024)
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https://aclanthology.org/2024.naacl-long.468.pdf
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