利用深层语言分析改进中文作文自动评分方法

利用自然语言处理技术对作文进行自动评阅是有重要意义和挑战的研究课题,引起了人工智能领域与教育领域学者的共同关注。该文聚焦于语文作文自动评分任务,提出通过深层语言分析,包括应用高性能别字、语法纠错器分析语言运用能力,采用自动修辞分析、优秀表达识别等手段反映语言表达能力,以及通过细粒度篇章质量分析评估篇章整体质量,来构建有效特征。该文同时提出了结合语言分析特征与深度神经网络编码的自适应混合评分模型。在真实语文作文数据上的实验表明,融入深层语言分析特征可有效提高作文评分效果;年级与主题自适应的模型训练策略,可提高模型的迁移能力和预测效果。消融实验进一步分析和解释了不同类型特征对评分效果的贡献。

Improving Chinese Automated Essay Scoring via Deep Language Analysis

Automated essay scoring is a significant and challenging research topic, which has attracted the attention of scholars in the fields of artificial intelligence and education. Focuses on Chinese automated essay scoring, this paper proposes to exploit deep language analysis, including the application of spelling error corrector and grammar error corrector to analyze grammar level writing ability, the automatic rhetorical analysis and excellent expression recognition to reflect language expression ability, and the fine-grained quality analysis of essay to evaluate overall quality. We then propose an adaptive hybrid scoring model, combining linguistic features and deep neural networks. The experimental results on Chinese student essay datasets show that 1) incorporating deep language analysis features can effectively improve the performance of automated essay scoring; and 2) the grade and topic adaptive training strategy also improves the transferring and predication abilities.