摘要：Oral English dialogue is a crucial part of a dialogue system that enables a computer to “understand” the input language as a human does, so the performance of a dialogue system is closely related to the performance of oral English dialogue understanding. In task-based human-machine dialogue systems, external knowledge bases can provide the machine with valid information beyond the training data, helping the model to better perform the oral English dialogue comprehension task. In this paper, we propose a deep recurrent neural network based on feature fusion, which directly stacks multiple nodes at a single time node to deepen the complexity of nonlinear transformations. The feature fusion network structure is applied to the ATIS dataset for oral English dialogue comprehension experiments, and the experimental results demonstrate that the feature fusion RNN network can further improve the effectiveness of the RNN network and the GRU network structure unit can obtain better results among different RNN node units.
