How we act, including how we speak, is more often than not determined by the situation we find ourselves in. We tailor dialogue to appropriately fit the scenario. If trained conversational agents are to continue evolving into dependable resources people can turn to for assistance, they'll need to be trained to do the same. Today, we're excited to make available the Intelligent Conversation Engine: Code and Pre-trained Systems, or Microsoft Icecaps, a new open-source toolkit that not only allows researchers and developers to imbue their chatbots with different personas, but also to incorporate other natural language processing features that emphasize conversation modeling. Icecaps provides an array of capabilities from recent conversation modeling literature.

General Purpose: We initially built this framework for Machine Translation, but have since used it for a variety of other tasks, including Summarization, Conversational Modeling, and Image Captioning. As long as your problem can be phrased as encoding input data in one format and decoding it into another format, you should be able to use or extend this framework. Usability: You can train a model with a single command. Several types of input data are supported, including standard raw text. Reproducibility: Training pipelines and models are configured using YAML files.