In this tutorial, we explore a fun and interesting use-case of recurrent sequence-to-sequence models. We will train a simple chatbot using movie scripts from the `Cornell Movie-Dialogs Corpus <https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html>`__. Conversational models are a hot topic in artificial intelligence research. Chatbots can be found in a variety of settings, including customer service applications and online helpdesks. These bots are often powered by retrieval-based models, which output predefined responses to questions of certain forms. In a highly restricted domain like a company’s IT helpdesk, these models may be sufficient, however, they are not robust enough for more general use-cases. Teaching a machine to carry out a meaningful conversation with a human in multiple domains is a research question that is far from solved. Recently, the deep learning boom has allowed for powerful generative models like Google’s `Neural Conversational Model <https://arxiv.org/abs/1506.05869>`__, which marks a large step towards multi-domain generative conversational models. In this tutorial, we will implement this kind of model in PyTorch.