ICLE++: Modeling Fine-Grained Traits for Holistic Essay Scoring
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Computer Science > Computation and Language
Title:ICLE++: Modeling Fine-Grained Traits for Holistic Essay Scoring
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.
| Comments: | Accepted as a long paper to NAACL 2024 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.27671 [cs.CL] |
| (or arXiv:2607.27671v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.27671
arXiv-issued DOI via DataCite (pending registration)
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| Related DOI: | https://doi.org/10.18653/v1/2024.naacl-long.468
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