TRUTH INFERENCE WITH BIPARTITE ATTENTION GRAPH NEURAL NETWORK FROM A COMPREHENSIVE VIEW

As crowdsourcing has cast a new solution to numerous tasks, truth inference, which deduces the accurate answer from massive noise labels (answers), has become quite an essential issue. However, existing proposals of truth inference only excel at limited tasks since they excessively depend on modeling either workers or labels with simple assumptions. In this paper, we propose BAT (Bipartite Attention-driven Truth) to flexibly infer the truth in various scenarios. The key behind BAT is to explore a comprehensive approach from the whole topology of crowdsourcing itself rather than any individual component. Specifically, BAT firstly characterizes the crowdsourcing as an attributed bipartite graph (ABG). Then it deploys a bipartite graph neural network (bi-GNN). The bi-GNN relies on a bipartite attention mechanism for exploiting the importance of different answers to compute the correct one. For verifying BAT, we compare BAT with other eight existing truth inference methods on real-world datasets from different domains (image, text, audio). The results show that BAT performs best on different crowdsourcing tasks.