Conceptual overview

Our goal is to enable AI-application developers and researchers with:

  • A set of pre-trained NLP models, pre-defined dialog system components (ML/DL/Rule-based), and pipeline templates;

  • A framework for implementing and testing their own dialog models;

  • Tools for application integration with adjacent infrastructure (messengers, helpdesk software, etc.);

  • Benchmarking environments for conversational models and uniform access to relevant datasets.


Key Concepts

  • A Skill fulfills the user’s goal in some domain. Typically, this is accomplished by presenting information or completing transaction (e.g. answer question by FAQ, booking tickets, etc.). However, for some tasks, a successful interaction is defined as continuous engagement (e.g. chit-chat).

  • A Model is any NLP model that doesn’t necessarily communicates with the user in natural language.

  • A Component is a reusable functional part of a Model or Skill.

  • Rule-based Models cannot be trained.

  • Machine Learning Models can be trained only stand alone.

  • Deep Learning Models can be trained independently and in an end-to-end mode being joined in a chain.

  • A Skill Manager performs selection of the Skill to generate the response.

  • A Chainer builds a model pipeline from heterogeneous components (Rule-based/ML/DL). It allows one to train and infer models in a pipeline as a whole.

The smallest building block of the library is a Component. A Component stands for any kind of function in an NLP pipeline. It can be implemented as a neural network, a non-neural ML model, or a rule-based system.

Components can be joined into a Model or a Skill. A Model solves a larger NLP task than a Component. However, in terms of implementation, Models are not different from Components. What differentiates a Skill from a Model is that the input and output of a Skill should both be strings. Therefore, Skills are usually associated with dialogue tasks.

DeepPavlov is built on top of the machine learning frameworks TensorFlow, Keras and PyTorch. Other external libraries can be used to build basic components.