Statistical Learning Models for Japanese Essay Scoring Toward One-shot Learning
Statistical Learning Models for Japanese Essay Scoring Toward One-shot Learning
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DOI：
10.1109/iiaiaai55812.2022.00070
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Chihiro Ejima;Koichi Takeuchi
中科院分区：
文献类型：
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作者：
Chihiro Ejima;Koichi Takeuchi
许多自动论文评分的研究都是使用机器学习模型进行的。以前的研究表明，使用机器学习模型对大规模文章进行评分具有很高的性能，然而，需要数百个评分答案来训练神经网络模型。在本文中，我们讨论了一次性学习的可能性，即只使用一个范文作为最高分数的训练样本。为此，我们应用回归模型来估计不同嵌入模型的作文分数，即BERT和基于词袋的编码模型。在初步实验中，使用UMAP对两种嵌入模型进行一次性学习的特征分析表明，与BERT编码模型相比，基于词袋的模型具有更大的潜力来对测试文章进行评分。因此，为了阐明基于词袋的编码模型的性能，我们进行了两个实验：首先，我们使用80%的分数文章作为训练数据来评估模型的性能，以估计测试文章的分数;其次，将一次性学习应用于模型。实验结果表明，提出的基于词袋的编码模型是有前途的。
A lot of studies of automatic essay scoring are conducted using machine learning models. The previous studies show high performance for scoring large scale essays with machine learning models, however, more than hundreds of scored answers are required to train the neural network models. In this paper we discuss the possibility of one-shot learning, that is, using only one model essay as a training sample of a highest score. For this purpose, we apply regression models to estimate essay scores with different embedding models, that are, BERT and bag-of-words based encoding models. In preliminary experiments, feature analyses of one-shot learning with UMAP for the two embedding models reveal that the bag-of-words based model has more potential to score the test essays comparing to the BERT encoding model. Thus, to clarify the performance of the bag-of-words based encoding model, we conduct two experiments: firstly, we evaluate the performance of models to estimate the scores of test essays using 80% of score essays are used as training data; secondly, one-shot learning is applied to the models. The experimental results show that the proposed bag-of-words based encoding model is promising.