基于孪生神经网络的行文一致性测评研究

针对目前的篇章级行文一致性度量模型只考虑了待测作文的全文行文一致性，无法捕捉文本语义块的隐含语义特征及其之间的一致性问题，提出了一种通用的作文行文一致性测评模型. 该模型借鉴孪生神经网络的思想，创新性地同时提取作文中核心人物的性格、形象特征以及故事情节特征并进行相似度度量，从而获取文本的中心思想以及行文一致性的匹配分数；使用无监督主题模型Biterm-LDA（Latent Dirichlet Allocation）对作文进行主题特征提取，解决了对手工标注的依赖。实验结果表明提出的模型评分与人工标注结果多数一致，且优于普通神经网络模型. 

Writing Consistency Evaluation Based on Siamese Neural Network

For current chapter graded consistency metric model only considers the full text consistency of the tested composition, and cannot capture the implicit semantic characteristics of the text language block and the consistency between them. In view of the above problems, a composition text consistency evaluation model was proposed for general compositions. Refering to the thoughts of the twin neural network, the model was arranged to extract the character, image characteristics, and storyline characteristics of the core character simultaneously in the composition and to perform similarity metrics, so as to obtain the central idea of the text and the matching score of the text consistency. A subject model Biterm-LDA（Latent Dirichlet Allocation）was used to extract the subject character of composition to avoid the dependence of the artificial labeling. The results show that the proposed model score is consistent with the results of artificial labeling, and is superior to ordinary neural network models.