A Bayesian Framework for Modeling Human Evaluations.

Several situations that we come across in our daily lives involve some form of evaluation: a process where an evaluator chooses a correct label for a given item. Examples of such situations include a crowd-worker labeling an image or a student answering a multiple-choice question. Gaining insights into human evaluations is important for determining the quality of individual evaluators as well as identifying true labels of items. Here, we generalize the question of estimating the quality of individual evaluators, extending it to obtain diagnostic insights into how various evaluators label different kinds of items. We propose a series of increasingly powerful hierarchical Bayesian models which infer latent groups of evaluators and items with the goal of obtaining insights into the underlying evaluation process. We apply our framework to a wide range of real-world domains, and demonstrate that our approach can accurately predict evaluator decisions, diagnose types of mistakes evaluators tend to make, and infer true labels of items.