@inproceedings{4e22610150bc4d5f88b637050f338ee3,
title = "Automatic chinese short answer grading with deep autoencoder",
abstract = "Short answer question is a common assessment type of teaching and learning. Automatic short answer grading is the task of automatically scoring short natural language responses. Most previous auto-graders mainly rely on target answers given by teachers. However, target answers are not always available. In this paper, a deep autoencoder based algorithm for automatic short answer grading is presented. The proposed algorithm can be built without expressly defining target answers, and learn the lower-dimensional representation of student responses. For the sake of reducing the influence of data imbalance, we introduce the expectation regularization term of label ratio into the model. The experimental results demonstrate the effectiveness of our proposed method.",
keywords = "Automatic grading, Deep autoencoder, Short answer, Text classification",
author = "Xi Yang and Yuwei Huang and Fuzhen Zhuang and Lishan Zhang and Shengquan Yu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 19th International Conference on Artificial Intelligence in Education, AIED 2018 ; Conference date: 27-06-2018 Through 30-06-2018",
year = "2018",
doi = "10.1007/978-3-319-93846-2\_75",
language = "英语",
isbn = "9783319938455",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "399--404",
editor = "Rose Luckin and Kaska Porayska-Pomsta and \{du Boulay\}, Benedict and Manolis Mavrikis and \{Penstein Ros{\'e}\}, Carolyn and Bruce McLaren and Roberto Martinez-Maldonado and Hoppe, \{H. Ulrich\}",
booktitle = "Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings",
address = "德国",
}