CGLAD: Using GLAD in Crowdsourced Large Datasets

In this article, we propose an improvement over the GLAD algorithm that increases the efficiency and accuracy of the model when working on problems with large datasets. The GLAD algorithm allows practitioners to learn from instances labeled by multiple annotators, taking into account the quality of their annotations and the instance difficulty. However, due to the number of parameters of the model, it does not scale easily to solve problems with large datasets, especially when the execution time is limited. Our proposal, CGLAD, solves these problems using clustering from vectors coming from the factorization of the annotation matrix. This approach drastically reduces the number of parameters in the model, which makes using GLAD strategy for solving multiple annotators problems easier to use and more efficient.