Abstract: With the continuous development of the human-machine dialogue system,it is of great significance for the computer to accurately understand the speaker's dialogue act and predict the act of response according to the history information of the dialogue.Previous research work focus on act prediction of responses based on dialogue text and existing labels.But in many scena-rios,the reply has not been generated.Therefore,this paper proposes a dialogue act prediction model based on reply generation.In the generation part,the Seq2Seq structure is used to generate text based on the conversation history information as text information for future replies in the conversation;in the classification part,the LSTM model is used to express the generated reply text and the existing conversation information as clause level representations.Combined with the attention mechanism,it highlights the connection between the dialogue sentence of the same round and the generated response.The experimental results show that the proposed model a chieves a 2.54% F1-score improvement compared to the simple baseline model,and the joint training method contributes to the improvement of model performance.

Key words: Attention mechanism, Dialogue act, Prediction model, Text generation