  For companies with customer service, mapping intents inside their
conversational data is crucial in building applications based on natural
language understanding (NLU). Nevertheless, there is no established automated
technique to gather the intents from noisy online chats or voice transcripts.
Simple clustering approaches are not suited to intent-sparse dialogues. To
solve this intent-landscape task, we propose an unsupervised pipeline that
extracts the intents and the taxonomy of intents from real-world dialogues. Our
pipeline mines intent-span candidates with an extractive Question-Answering
Electra model and leverages sentence embeddings to apply a low-level density
clustering followed by a top-level hierarchical clustering. Our results
demonstrate the generalization ability of an ELECTRA large model fine-tuned on
the SQuAD2 dataset to understand dialogues. With the right prompting question,
this model achieves a rate of linguistic validation on intent spans beyond 85%.
We furthermore reconstructed the intent schemes of five domains from the
MultiDoGo dataset with an average recall of 94.3%.
