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

Yusen Zhang, Zhongli Li, Qingyu Zhou, Ziyi Liu, Chao Li, Mina Ma, Yunbo Cao, Hongzhi Liu


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.
Anthology ID:
2022.coling-1.269
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3042–3053
Language:
URL:
https://aclanthology.org/2022.coling-1.269
DOI:
Bibkey:
Cite (ACL):
Yusen Zhang, Zhongli Li, Qingyu Zhou, Ziyi Liu, Chao Li, Mina Ma, Yunbo Cao, and Hongzhi Liu. 2022. AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3042–3053, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications (Zhang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.269.pdf
Code
 yusenzhang826/aim