- The paper introduces Supporting Clustering with Contrastive Learning (SCCL), a novel method enhancing unsupervised short text clustering by combining contrastive and clustering objectives.
- SCCL employs a neural network with dual heads for instance-wise contrastive loss and clustering loss, optimizing both instance separation and top-down grouping.
- Empirical evaluations on eight benchmark datasets demonstrate SCCL consistently improves accuracy (3-11%) and normalized mutual information (4-15%) over state-of-the-art methods, particularly for short text.
Supporting Clustering with Contrastive Learning
The paper "Supporting Clustering with Contrastive Learning" introduces a novel approach, Supporting Clustering with Contrastive Learning (SCCL), tailored to enhance unsupervised clustering by leveraging the principles of contrastive learning. The research primarily focuses on short text clustering—a domain marked by the complexities arising from noise and sparsity typical to social media and web contexts.
Technical Framework
SCCL operates by integrating contrastive learning mechanisms alongside traditional clustering objectives. The model architecture consists of a neural network responsible for mapping input data into a representation space, augmented by two distinct heads employed for contrastive loss and clustering loss respectively. Together, these components foster a balanced optimization that exploits the advantages of both instance discrimination and top-down clustering techniques.
Contrastive Learning Model: SCCL utilizes Instance-wise Contrastive Learning (Instance-CL) which disperses different data instances across the representation space while implicitly grouping similar instances. This optimization is achieved via contrastive losses computed on augmented data, promoting scattered overlapping categories and enabling clustering to enforce tighter intra-cluster bindings.
Clustering Method: By employing a top-down clustering loss, SCCL forms high-level semantic groupings in the data. The clustering model utilizes the Student’s t-distribution to assign probabilistic cluster affiliations, refining these assignments through optimization of KL divergence between actual distribution and idealized target distributions.
Empirical Evaluation
The empirical validation of SCCL spans eight benchmark datasets primarily involving short text clustering tasks. The framework demonstrates consistent improvements over multiple state-of-the-art methods, advancing accuracy by margins of 3% to 11% and normalized mutual information gains of 4% to 15%. Notable datasets like SearchSnippets, StackOverflow, and GoogleNews reveal its competitive edge, although challenges persist in medical-domain specific data such as Biomedical datasets.
Investigative Insights
Through a detailed analysis comprising ablation studies, SCCL’s efficacy is dissected. The mutual strength of instance discrimination and clustering reveals improved separability within learned clusters, reducing intra-cluster variance while enhancing inter-cluster distances. These insights underline SCCL’s ability to overcome inherent overlaps in high-dimensional representation spaces—central to effective unsupervised clustering.
Future Directions
As SCCL showcases versatility in handling short text data, it opens avenues for broader applications in varied text clustering scenarios. Moreover, the right balance of augmentation techniques remains crucial, hinting at potential improvements by exploring advanced augmentation strategies. This suggests scope for enhancing robustness and generalization in SCCL’s architecture, particularly in domains with sparse semantic signals.
The research encapsulates significant contributions towards facilitating better computational models for short text clustering, underlining the profound synergy between contrastive learning and clustering objectives. It advances foundational understanding while laying groundwork for future explorations into augmentations, computational efficiencies, and multi-domain adaptations.