Abstract
A deep learning model combining multiple neural networks and attentional mechanisms is proposed to solve the problem of answer ranking. The word vectors of the questions and candidate answers were sent to the Convolutional Neural Network for learning, which is used the Leaky Relu activation function, and the learning results were pieced together with four Attention items, and features in relation to the vocabulary and topic, and then input into the Bidirectional Gated Recurrent Units. After the output results were processed by Multi-layer Perception, the softmax classifier produced the final ranking results. Experimental results indicate satisfactory performance of the model on WikiQACorpus data set with an accuracy of 80.86%.
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Duan, L., Zhang, J., Wang, L., Gao, J., Li, A. (2021). An Answer Sorting Method Combining Multiple Neural Networks and Attentional Mechanisms. In: Mei, H., et al. Big Data. BigData 2020. Communications in Computer and Information Science, vol 1320. Springer, Singapore. https://doi.org/10.1007/978-981-16-0705-9_7
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