Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues
This work addresses the need for scalable intent induction in customer support, offering a solution that eliminates annotation costs, though it appears incremental as it builds on existing unsupervised methods.
The authors tackled the problem of intent induction in customer support dialogues without requiring annotated datasets, proposing a fully unsupervised framework that includes dialogue pre-processing and sequence analysis, achieving results applicable to any use case.
State of the art models in intent induction require annotated datasets. However, annotating dialogues is time-consuming, laborious and expensive. In this work, we propose a completely unsupervised framework for intent induction within a dialogue. In addition, we show how pre-processing the dialogue corpora can improve results. Finally, we show how to extract the dialogue flows of intentions by investigating the most common sequences. Although we test our work in the MultiWOZ dataset, the fact that this framework requires no prior knowledge make it applicable to any possible use case, making it very relevant to real world customer support applications across industry.