Context Understanding and Response Algorithm of Chat Robot Based on Enhanced Seq2seq Model

With the explosive growth of information, people’s desire to quickly obtain the correct information is becoming stronger. In order to meet people’s needs for faster access to information and more human communication with machines, question answering systems have entered people’s vision. As a branch of question answering systems, chat robots have also rapidly developed. This article conducts research on context understanding and response algorithms for chat robots based on the enhanced seq2seq model. The response algorithm allows the robot to remember what the topic we just talked about was, and whether the sentence entered by the user is related to the current topic. If relevant, the current topic is added or completed into the user's input statement, thus enabling the chat robot to have a certain contextual understanding function. Through simulation experiments, it can be seen that the loss function of traditional models fluctuates greatly during the iteration process. Finally, the loss function values tend to stabilize when they are around 1.5. The loss function of this model decays faster and is more stable during the iteration process. Finally, the loss function values tend to be around 1, indicating that the performance of this model is better.