Towards a Fully Unsupervised Framework for Intent Induction in Customer
  Support Dialogues
Rita Costa Bruno Martins Sérgio Viana Luisa Coheur
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.