NLP models for deep learning

The model is capable of creating simple dialogues based on an extensive conversational training set, is able to extract knowledge from highly specialized datasets, as well as large and noisy general subtitle datasets for films. In the highly specialized area of ​​helpdesk for IT solutions, the model finds solutions to a technical problem through dialogue. On noisy movie transcript datasets, the model is able to make simple reasoning based on common sense.

Question-answer (QA) systems

The idea of ​​question-answering (hereinafter - QA) systems is to extract information directly from a document, conversation, online search or any other place that meets the user's needs. Instead of forcing the user to read the full text, QA systems prefer to give short and concise answers. Today QA systems can be easily combined with chatbots, go beyond searching for text documents and extract information from a set of pictures like in doctranslator.

Deep learning

Most NLP problems can be viewed as question-and-answer problems. The paradigm is simple: a request is sent, to which the machine provides a response. By reading a text or a set of instructions, an intelligent system must find the answer to a wide range of questions. Naturally, you need to create a model for answering common questions.

Especially for QA tasks, a powerful deep learning architecture has been created and optimized - the Dynamic Memory Network (hereinafter referred to as DNM). Trained on a training set from inputs and questions, DNM builds up episodic memories and uses them to generate appropriate responses.