Effective Sampling for Large-scale Automated Writing Evaluation Systems

Nicholas Dronen, P. Foltz, Kyle Habermehl
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引用次数: 17

Abstract

Automated writing evaluation (AWE) has been shown to be an effective mechanism for quickly providing feedback to students. It has already seen wide adoption in enterprise-scale applications and is starting to be adopted in large-scale contexts. Training an AWE model has historically required a single batch of several hundred writing examples and human scores for each of them. This requirement limits large-scale adoption of AWE since human-scoring essays is costly. Here we evaluate algorithms for ensuring that AWE models are consistently trained using the most informative essays. Our results show how to minimize training set sizes while maximizing predictive performance, thereby reducing cost without unduly sacrificing accuracy. We conclude with a discussion of how to integrate this approach into large-scale AWE systems.
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大规模自动写作评价系统的有效抽样
自动写作评估(AWE)已被证明是一种快速向学生提供反馈的有效机制。它已经在企业级应用程序中被广泛采用,并开始在大规模环境中被采用。从历史上看,训练一个AWE模型需要几百个写作示例和每个示例的人工分数。这一要求限制了AWE的大规模采用,因为人工评分的成本很高。在这里,我们评估算法,以确保使用最具信息量的文章始终如一地训练AWE模型。我们的结果显示了如何在最大化预测性能的同时最小化训练集大小,从而在不过度牺牲准确性的情况下降低成本。最后,我们讨论了如何将这种方法集成到大规模AWE系统中。
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