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To automatically correct handwritten assignments, the traditional approach isto use an OCR model to recognize characters and compare them to answers. TheOCR model easily gets confused on recognizing handwritten Chinese characters,and the textual information of the answers is missing during the modelinference. However, teachers always have these answers in mind to review andcorrect assignments. In this paper, we focus on the Chinese cloze testscorrection and propose a multimodal approach (named AiM). The encodedrepresentations of answers interact with the visual information of students'handwriting. Instead of predicting 'right' or 'wrong', we perform the sequencelabeling on the answer text to infer which answer character differs from thehandwritten content in a fine-grained way. We take samples of OCR datasets asthe positive samples for this task, and develop a negative sample augmentationmethod to scale up the training data. Experimental results show that AiMoutperforms OCR-based methods by a large margin. Extensive studies demonstratethe effectiveness of our multimodal approach.

AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications