Self-Supervised Hierarchical Representation Learning for Multi-Dimension Context

Abstract

Self-supervised hierarchical representation learning offers an effective approach to capturing multi-dimensional context from unlabeled data. A key challenge in representation learning is integrating information from diverse aspects of the input, particularly when labeled data is limited. To address this, a novel strategy can be introduced that learns representations hierarchically, enabling the capture of context at varying levels of abstraction and across multiple dimensions. The process begins by modeling different contextual facets through component-specific representations, each capturing distinct semantic and structural attributes. A dynamic aggregation mechanism then combines these representations in a hierarchical manner, allowing information to propagate across levels of contextual abstraction. This enables the encoding of both fine-grained nuances and broader contextual dependencies. By leveraging self-supervised learning, the approach optimizes for inherent relationships within the multi-dimensional context, enabling the acquisition of robust representations from unlabeled data. This makes it particularly suitable for domains where labeled data is scarce or costly to obtain. Experimental results highlight the ability to learn rich, hierarchical representations that enhance performance on downstream tasks requiring deep contextual understanding. Key technical contributions include: (1) a context-aware masking strategy using Text Encoder for semantic recovery of masked fields,  (2) a Hierarchical Model that fuses fine-grained tabular features with coarse-grained concepts and (3) a multi-stage training code base combining contrastive loss for cross-document alignment (RFP-bid pairs) and silhouette scores (from scikit-learn) to validate cluster coherence.