Robust Representation Learning with Reliable Pseudo-labels Generation
  via Self-Adaptive Optimal Transport for Short Text Clustering
Xiaolin Zheng Mengling Hu Weiming Liu Chaochao Chen Xinting Liao
Short text clustering is challenging since it takes imbalanced and noisy data
as inputs. Existing approaches cannot solve this problem well, since (1) they
are prone to obtain degenerate solutions especially on heavy imbalanced
datasets, and (2) they are vulnerable to noises. To tackle the above issues, we
propose a Robust Short Text Clustering (RSTC) model to improve robustness
against imbalanced and noisy data. RSTC includes two modules, i.e.,
pseudo-label generation module and robust representation learning module. The
former generates pseudo-labels to provide supervision for the later, which
contributes to more robust representations and correctly separated clusters. To
provide robustness against the imbalance in data, we propose self-adaptive
optimal transport in the pseudo-label generation module. To improve robustness
against the noise in data, we further introduce both class-wise and
instance-wise contrastive learning in the robust representation learning
module. Our empirical studies on eight short text clustering datasets
demonstrate that RSTC significantly outperforms the state-of-the-art models.
The code is available at: https://github.com/hmllmh/RSTC.