Automatic Discovery of Novel Intents & Domains from Text Utterances

TL;DR

This paper introduces ADVIN, a framework that automatically discovers new intents and domains from unlabeled text data, addressing the challenge of evolving language use in real-world applications.

Contribution

ADVIN is a novel unsupervised approach that identifies and hierarchically links new intents and domains without prior labels, outperforming existing methods.

Findings

ADVIN outperforms baselines on benchmark datasets.

It effectively discovers multiple latent intent categories.

The framework successfully handles real user utterances.

Abstract

One of the primary tasks in Natural Language Understanding (NLU) is to recognize the intents as well as domains of users' spoken and written language utterances. Most existing research formulates this as a supervised classification problem with a closed-world assumption, i.e. the domains or intents to be identified are pre-defined or known beforehand. Real-world applications however increasingly encounter dynamic, rapidly evolving environments with newly emerging intents and domains, about which no information is known during model training. We propose a novel framework, ADVIN, to automatically discover novel domains and intents from large volumes of unlabeled data. We first employ an open classification model to identify all utterances potentially consisting of a novel intent. Next, we build a knowledge transfer component with a pairwise margin loss function. It learns discriminative deep features to group together utterances and discover multiple latent intent categories within them in an unsupervised manner. We finally hierarchically link mutually related intents into domains, forming an intent-domain taxonomy. ADVIN significantly outperforms baselines on three benchmark datasets, and real user utterances from a commercial voice-powered agent.