Context: it is important for CC analysts to quickly understand what topics are included in the dialogue corpus in order to quickly automate their work. Manually dividing such cases into topics is a labour-intensive task.
Decision: a system of hierarchical clustering of contact centre dialogues is proposed, which allows creating a taxonomy of intents.
Results:
Integration into the BI Department for automation of customer contact centres:
- Reducing the load on the analyst to allocate automation classes by 80%.
Harmonization of taxonomy:
- Reducing the time to identify a new category by 60%;
- Allocation of new intents in the flow of requests with a quality of 70%.
The selection of entities and the analysis of interpretability:
- Selecting named entities and filling in the client card is 10% more accurate;
- Increase in the share of interpreted intents relative to the old model by 40%.
To build the model, we used:
- Dialogues for multiple contact centres;
- Assessor markup of paraphrases for each dataset.
Simulation result:
- Model for soft hierarchical clustering of dialogues;
- Final taxonomy of categories with a description;
- A server-side custom application with an API interface.
Customer: Contact Center, Telecom
Technology stack: TopicNet, BigARTM, Flask, Python, PyTorch.