  We present our work on Track 2 in the Dialog System Technology Challenges 11
(DSTC11). DSTC11-Track2 aims to provide a benchmark for zero-shot,
cross-domain, intent-set induction. In the absence of in-domain training
dataset, robust utterance representation that can be used across domains is
necessary to induce users' intentions. To achieve this, we leveraged a
multi-domain dialogue dataset to fine-tune the language model and proposed
extracting Verb-Object pairs to remove the artifacts of unnecessary
information. Furthermore, we devised the method that generates each cluster's
name for the explainability of clustered results. Our approach achieved 3rd
place in the precision score and showed superior accuracy and normalized mutual
information (NMI) score than the baseline model on various domain datasets.
