Dialogue clustering system
Project: contact center analytics automation project - semantic clustering of dialogues
Project with MC NTT
(Rostelecom)
DESCRIPTION

Automating the responses of contact center operators assumes that there is a taxonomy of issues that customers address. This taxonomy will allow you to classify requests and then process them. When working with a large number of contact centers on various topics, you need a system for rapid analysis of the body of dialogues. You need to create a tool for automatically building ready-made taxonomies for dialog boxes.

RELEVANCE

It is important for KC analysts to quickly understand what is in the dialog corpus in order to quickly automate their work. Building such a taxonomy completely manually is a very time-consuming task that requires automation.
The decision of the team
We asked the partner for a marked-up selection of synonymous dialogs, which helped us compare different models and configure the model parameters for a specific task.
We tested several methods for solving the problem: various neural network approaches to searching for paraphrases and hierarchical multi-modal thematic models. Thematic models performed better.
The final solution was packaged in a Docker container that implements the business logic required by the partner.
  • Results
    - Reducing the load on the analyst
    - Reducing the time to identify new categories
    - Identification of new intents in the flow of requests
  • Permitted difficulties
    - Model that is resistant to changing subjects
    - Stability of the model when changing the size of the text case
    - Correction of typos (including for cases with very specific vocabulary)
  • Team
    - Project Manager: Alexey Goncharov
    - Team Lead: Artem Popov
    - Team of researchers: Daria Polyudova, Evgenia Veselova, Viktor Bulatov
    - Scientific consultant of the project: Konstantin Vorontsov
  • Technology stack
    TopicNet, BigARTM, Flask, Python, PyTorch, gensim, UMAP
Made on
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