IDAS: Intent Discovery with Abstractive Summarization
Maarten De Raedt Fréderic Godin Thomas Demeester Chris Develder
Intent discovery is the task of inferring latent intents from a set of
unlabeled utterances, and is a useful step towards the efficient creation of
new conversational agents. We show that recent competitive methods in intent
discovery can be outperformed by clustering utterances based on abstractive
summaries, i.e., "labels", that retain the core elements while removing
non-essential information. We contribute the IDAS approach, which collects a
set of descriptive utterance labels by prompting a Large Language Model,
starting from a well-chosen seed set of prototypical utterances, to bootstrap
an In-Context Learning procedure to generate labels for non-prototypical
utterances. The utterances and their resulting noisy labels are then encoded by
a frozen pre-trained encoder, and subsequently clustered to recover the latent
intents. For the unsupervised task (without any intent labels) IDAS outperforms
the state-of-the-art by up to +7.42% in standard cluster metrics for the
Banking, StackOverflow, and Transport datasets. For the semi-supervised task
(with labels for a subset of intents) IDAS surpasses 2 recent methods on the
CLINC benchmark without even using labeled data.