Conceptual overview

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

  • 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 environment for conversational models and uniform access to relevant datasets.


Key Concepts

  • Skill fulfills 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 success of interaction is defined as continuous engagement (e.g. chit-chat).

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

  • Component is a reusable functional part of 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.

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

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

The smallest building block of the library is Component. 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. Model solves a larger NLP task compared to Component. However, in terms of implementation Modelss are not different from Components. The difference of a Skill from a Model is that its input and output should both be strings. Therefore, Skills are usually associated with dialogue tasks.

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