Answer Selection Based on Mixed Embedding and Composite Features

摘要

With the rapid growth of text information, intelligent question answering has gained more attention than ever. In this paper, we focus on answer selection, one kind of question answering tasks. In this field, deep neural networks and attention mechanism have brought encouraging results. To improve the performance further, we investigate mixed embedding (word embedding and character embedding) representation for sentences to encode rich meaning. At the same time, we introduce a convolutional neural network (CNN) to compensate the loss of the max pooling layer in our attention based bidirectional Long Short-Term Memory (biLSTM) model. CNN features and the features from max pooling form final composite features, which are employed to select correct answers. Experimental results show that we can obviously improve the Mean Reciprocal Rank (MRR) performance by 6.0% with the help of mixed embedding and composite features. The MRR and ACC@1 score are 79.63% and 69.60% respectively.