构建新数据集ICLE++，用于更全面的作文自动评分研究。
ICLE++: Modeling Fine-Grained Traits for Holistic Essay Scoring
- 构建包含整体分与细粒度特质分的说服性作文数据集
- 支持跨数据集泛化能力评估及多特质评分任务测试
- 适合从事自动作文评分、教育人工智能的研究者
近期大多数自动作文评分（AES）模型仅在ASAP语料库上进行评估，但ASAP存在局限性，例如难以判断模型在其他语料上的泛化能力。为此，我们推出了ICLE++，一个包含说服性学生作文的数据集，每篇作文均标注了整体评分和细粒度特质分数。该数据集不仅可用于检验在ASAP上训练的模型在其他数据上的泛化性能，还可支持多特质评分与跨提示评分等新型AES任务的评估。我们相信，作为长期对ICLE语料库作文标注工作的成果，ICLE++为急需的高质量标注语料库建设提供了重要贡献。
原文摘要 · Abstract (English)
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.
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