While utilizing a bipartite graph for text-label matching offers several benefits such as modeling both text-label alignment and label-text alignment efficiently during fine-tuning stages within frameworks like MatchXML, there are potential limitations or drawbacks associated with this approach:
Scalability: Bipartite graphs can become computationally expensive when dealing with extremely large datasets containing millions of samples and labels. As the number of edges grows exponentially with dataset size, it may lead to increased memory usage and longer computation times.
Complexity: Managing edge connections between text samples and labels within a bipartite graph requires careful handling to ensure accurate alignment during training processes. Complex algorithms may be needed for efficient traversal and optimization across multiple layers or stages.
Overfitting: Bipartite graphs could potentially introduce overfitting issues if not properly regularized or optimized during training phases. Balancing model complexity with generalization capabilities becomes crucial when working with intricate graph structures.
Interpretability: Interpreting results from models trained using bipartite graphs might pose challenges due to the inherent complexity introduced by interconnected nodes representing texts and labels.