Existing learning-from-crowds methods aim to design proper aggregation strategies to infer the unknown true labels from noisy labels provided by crowdsourcing. They treat the ground truth as hidden variables and use statistical or deep learning based worker behavior models to infer the ground truth. However, worker behavior models that rely on ground truth hidden variables overlook workers' behavior at the item feature level, leading to imprecise characterizations and negatively impacting the quality of learning-from-crowds. This paper proposes a new paradigm of multi-task supervised learning-from-crowds, which eliminates the need for modeling of items's ground truth in worker behavior models. Within this paradigm, we propose a worker behavior model at the item feature level called Mixture of Experts based Multi-task Supervised Learning-from-Crowds (MMLC), then, two aggregation strategies are proposed within MMLC. The first strategy, named MMLC-owf, utilizes clustering methods in the worker spectral space to identify the projection vector of the oracle worker. Subsequently, the labels generated based on this vector are regarded as the items's ground truth The second strategy, called MMLC-df, employs the MMLC model to fill the crowdsourced data, which can enhance the effectiveness of existing aggregation strategies . Experimental results demonstrate that MMLC-owf outperforms state-of-the-art methods and MMLC-df enhances the quality of existing learning-from-crowds methods.

In crowdsourcing, existing efforts mainly use real datasets collected from crowdsourcing as test datasets to evaluate the effectiveness of aggregation algorithms. However, these work ignore the fact that the datasets obtained by crowdsourcing are usually sparse and imbalanced due to limited budget. As a result, applying the same aggregation algorithm on different datasets often show contradicting conclusions. For example, on the RTE dataset, Dawid and Skene model performs significantly better than Majority Voting, while on the LableMe dataset, the experiments give the opposite conclusion. It is challenging to obtain comprehensive and balanced datasets at a low cost. To our best knowledge, little effort have been made to the fair evaluation of aggregation algorithms. To fill in this gap, we propose a novel method named TDG4Crowd that can automatically generate comprehensive and balanced datasets. Using Kullback Leibler divergence and Kolmogorov–Smirnov test, the experiment results show the superior of our method compared with others. Aggregation algorithms also perform more consistently on the synthetic datasets generated using our method.

End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e.g., instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art.

Knowledge acquisition (e.g. through labeling) is one of the most successful applications in crowdsourcing. In practice, collecting as specific as possible knowledge via crowdsourcing is very useful since specific knowledge can be generalized easily if we have a knowledge base, but it is difficult to infer specific knowledge from general knowledge. Meanwhile, tasks for acquiring more specific knowledge can be more difficult for workers, thus need more answers to infer high-quality results. Given a limited budget, assigning workers to difficult tasks will be more effective for the goal of specific knowledge acquisition. However, existing crowdsourcing task scheduling cannot incorporate the specificity of workers' answers. In this paper, we present a new framework for task scheduling with the limited budget, targeting an effective solution to more specific knowledge acquisition. We propose novel criteria for evaluating the quality of specificity-dependent answers and result inference algorithms to aggregate more specific answers with budget constraints. We have implemented our framework with real crowdsourcing data and platform, and have achieved significant performance improvement compared with existing approaches.