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

TL;DR

This paper introduces AiM, a multimodal method for correcting handwritten Chinese cloze tests by integrating answer information with handwriting visuals, outperforming traditional OCR-based approaches.

Contribution

The paper presents a novel multimodal approach that combines answer representations with handwriting images for more accurate Chinese cloze test correction.

Findings

AiM significantly outperforms OCR-based methods.

Negative sample augmentation improves training data quality.

Multimodal interaction enhances correction accuracy.

Abstract

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