Chinese Content Scoring: Open-Access Datasets and Features on Different Segmentation Levels

Yuning Ding1, Andrea Horbach2, Torsten Zesch3
1University Duisburg Essen, 2University of Duisburg-Essen, 3Language Technology Lab, University of Duisburg-Essen


In this paper, we analyse the challenges of Chinese content scoring in comparison to English. As a review of prior work for Chinese content scoring shows a lack of open-access data in the field, we present two short-answer data sets for Chinese. The Chinese Educational Short Answers data set (CESA) contains 1800 student answers for five science-related questions. As a second data set, we collected ASAP-ZH with 942 answers by re-using three existing prompts from the ASAP data set.

We adapt a state-of-the-art content scoring system for Chinese and evaluate it in several settings on these data sets. Results show that features on lower segmentation levels such as character n-grams tend to have better performance than features on token level.