Essay scoring is a complicated processing requiring analyzing, summarizing and judging expertise. Traditional work on essay scoring focused on automatic handcrafted features, which are expensive yet sparse. Neural models offer a way to learn syntactic and semantic features automatically, which can potentially improve upon discrete features. In this paper, we employ convolutional neural network (CNN) for the effect of automatically learning features, and compare the result with the state-of-art discrete baselines. For in-domain and domain-adaptation essay scoring tasks, our neural model empirically outperforms discrete models.
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