The framework needs to be provided with a dataset (RASA or DSTC2), train the model, download it, and then use it by either calling them natively from Python or by rising it as microservices and then calling them via its standard DeepPavlov REST API. Currently, DeepPavlov support two ways to define domain model and behavior of a given goal-oriented skill -- RASA (domain.yml, The link to the notebook is below.

Dialogue systems have recently become a standard in human-machine interaction, with chatbots appearing in almost every industry to simplify the interaction between people and computers. They can be integrated into websites, messaging platforms, and devices. Chatbots are on the rise, and companies are choosing to delegate routine tasks to chatbots rather than humans, thus providing huge labor cost savings. Unlike humans, chatbots are capable of processing multiple user requests at a time and are always available. However, many companies don't know where to start when developing a bot to meet their business needs.

Dialogue systems have recently become a standard in human-machine interaction, with chatbots appearing in almost every industry to simplify the interaction between people and computers. They can be integrated into websites, messaging platforms, and devices. Chatbots are on the rise, and companies are choosing to delegate routine tasks to chatbots rather than humans, thus providing huge labor cost savings. Unlike humans, chatbots are capable of processing multiple user requests at a time and are always available. However, many companies don't know where to start when developing a bot to meet their business needs.

There are currently few datasets appropriate for training and evaluating models for non-goal-oriented dialogue systems (chatbots); and equally problematic, there is currently no standard procedure for evaluating such models beyond the classic Turing test. The aim of our competition is therefore to establish a concrete scenario for testing chatbots that aim to engage humans, and become a standard evaluation tool in order to make such systems directly comparable. This is the second Conversational Intelligence (ConvAI) Challenge. The previous one was conducted under the scope of NIPS 2017 Competitions track. The winning entry will receive $20,000 in Mechanical Turk funding – in order to encourage further data collection for dialogue research.

Vocab is a trainable class, which forms and serialize vocabs.