Decoupling Pseudo Label Disambiguation and Representation Learning for
  Generalized Intent Discovery
Yutao Mou Xiaoshuai Song Keqing He Chen Zeng Pei Wang Jingang Wang Yunsen Xian Weiran Xu
Generalized intent discovery aims to extend a closed-set in-domain intent
classifier to an open-world intent set including in-domain and out-of-domain
intents. The key challenges lie in pseudo label disambiguation and
representation learning. Previous methods suffer from a coupling of pseudo
label disambiguation and representation learning, that is, the reliability of
pseudo labels relies on representation learning, and representation learning is
restricted by pseudo labels in turn. In this paper, we propose a decoupled
prototype learning framework (DPL) to decouple pseudo label disambiguation and
representation learning. Specifically, we firstly introduce prototypical
contrastive representation learning (PCL) to get discriminative
representations. And then we adopt a prototype-based label disambiguation
method (PLD) to obtain pseudo labels. We theoretically prove that PCL and PLD
work in a collaborative fashion and facilitate pseudo label disambiguation.
Experiments and analysis on three benchmark datasets show the effectiveness of
our method.