Abstract
Neural network models have played an important role in text applications, such as document summaries and automatic short answer questions. In previous existing works, questions and answers are together used as input in recurrent neural networks (RNN) and convolutional neural networks (CNN), then output corresponding scores. This paper presents a method for measuring the score for short answer questions and answers. This paper makes scoring by establishing a hierarchical word-sentence model to represent questions and answers and using the attention mechanism to automatically determine the relative weight of questions and answers. Firstly, the model combines CNN and Bidirectional Long Short-Term Memory Networks (BLSTM) to extract the semantic features of questions and answers. Secondly, it captures the representation vector of relevant questions and answers from the sentence-level features. Finally, all feature vectors are concatenated and input to the output layer to obtain the corresponding score. Experiment results show that the model in this paper is better than multiple baselines.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Qi, H., Wang, Y., Dai, J., Li, J., Di, X. (2019). Attention-Based Hybrid Model for Automatic Short Answer Scoring. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-32216-8_37
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DOI: https://doi.org/10.1007/978-3-030-32216-8_37
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