A Chinese Oral Question-and-Answering System Based on LSTM

We have developed a new answering system, which recognize questions inputs in natural language and respond accordingly. We implement a deep learning method to understand conversation, dialogue and sort out a summary of narrative. Many papers have already shown how to combine and apply various tools for similar problems, however, only few of them mentioned how to effectively reduce the error rate of speech recognition and combine this part to answering system. In this paper, we combine these two functional parts and minimize the size of the model, which does not influence the accuracy of it. This model has been implemented in embedded system.