The paper introduces a hierarchical latent variable encoder-decoder model, called VHRED, designed to generate dialogues. The model addresses the limitations of shallow generation processes in existing models by incorporating stochastic latent variables that span a variable number of time steps. These latent variables facilitate the generation of meaningful, long, and diverse responses while maintaining dialogue context. The model is evaluated using a human evaluation study on the Twitter Dialogue Corpus, demonstrating superior performance compared to competing models such as LSTM and HRED. VHRED's hierarchical structure allows it to model higher-level variability and generate more on-topic, semantically rich responses, particularly in longer contexts. The paper also discusses related work and potential applications of the model in other sequential generation tasks.The paper introduces a hierarchical latent variable encoder-decoder model, called VHRED, designed to generate dialogues. The model addresses the limitations of shallow generation processes in existing models by incorporating stochastic latent variables that span a variable number of time steps. These latent variables facilitate the generation of meaningful, long, and diverse responses while maintaining dialogue context. The model is evaluated using a human evaluation study on the Twitter Dialogue Corpus, demonstrating superior performance compared to competing models such as LSTM and HRED. VHRED's hierarchical structure allows it to model higher-level variability and generate more on-topic, semantically rich responses, particularly in longer contexts. The paper also discusses related work and potential applications of the model in other sequential generation tasks.