Research on Question Answering System Based on Bi-LSTM and Self-attention Mechanism
Hao Xiang, Jinguang Gu · 2020
With the development of artificial intelligence technology, intelligent question answering has become a hot research direction in the field of natural language processing. This paper proposes a question answering method based on Bi-LSTM and self-attention mechanism model. This method uses Bi-LSTM to encode and align the question and answer respectively, then uses self-attention to obtain the relationship between keywords, and finally performs softmax through the fully connected layer to obtain the similarity between the question and answer. Finally, in the experiment, compared with the traditional attention model, the accuracy rate of this model was increased by 1.6%, and the recall rate was increased by 1.5%.