Semi-supervised partial label learning (SSPLL) aims to improve the generalization performance of partial label (PL) classifiers by effectively leveraging unlabeled data. Nevertheless, the inherent ambiguity in supervision, where the ground-truth label of a PL example is hidden within a set of candidate labels, poses significant challenges. The presence of false positive labels potentially misleads model's judgment, resulting in pronounced confirmation bias. To address these issues, we propose a novel approach named CODUAL, which jointly learns a pair of dual representations for each instance: the predictive class distribution and the low-dimensional embedding. The dual representations interact and progress collaboratively during training. On one hand, in the embedding space the class prototypes are derived via solving a tailored empirical distance minimization problem and employed to smooth the pseudo-targets of unlabeled instances. On the other hand, the refined class distributions regularize the embedding space via encouraging instances with similar pseudo-targets to exhibit similar embeddings. Through an in-depth analysis, we provide-to the best of our knowledge-the first theoretical explanation of how collaborative dual representations facilitate more effective use of unlabeled data for disambiguation. Extensive experiments over benchmark datasets validate the superiority of our proposed approach.