Q&A Label Learning
Kota Kawamoto Masato Uchida
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.