Q&A Label Learning
This addresses the challenge of label generation in machine learning by deriving models from annotation procedures rather than assuming them, though it appears incremental as it builds on prior work.
The paper tackles the problem of label assignment for supervised machine learning by proposing Q&A labeling, a novel annotation method where a question generator asks about labels and an annotator answers to assign them, and shows that learning with these labels achieves statistical consistency.
Assigning labels to instances is crucial for supervised machine learning. In this paper, we proposed a novel annotation method called Q&A labeling, which involves a question generator that asks questions about the labels of the instances to be assigned, and an annotator who answers the questions and assigns the corresponding labels to the instances. We derived a generative model of labels assigned according to two different Q&A labeling procedures that differ in the way questions are asked and answered. We showed that, in both procedures, the derived model is partially consistent with that assumed in previous studies. The main distinction of this study from previous studies lies in the fact that the label generative model was not assumed, but rather derived based on the definition of a specific annotation method, Q&A labeling. We also derived a loss function to evaluate the classification risk of ordinary supervised machine learning using instances assigned Q&A labels and evaluated the upper bound of the classification error. The results indicate statistical consistency in learning with Q&A labels.