Meta Label Correction for Learning with Weak Supervision

Guoqing Zheng, Ahmed Hassan Awadallah, Susan Dumais

Abstract:

Leveraging weak or noisy supervision for building effective machine learning
models has long been an important research problem. The growing need
for large-scale datasets to train deep learning models has increased
its importance. Weak or noisy supervision could originate from
multiple sources including non-expert annotators or automatic labeling
based on heuristics or user interaction signals. Previous work on
modeling and correcting weak labels have been focused on various
aspects, including loss correction, training instance re-weighting,
etc. In this paper, we approach this problem from a novel perspective
based on meta-learning. We view the label correction procedure as a
meta-process and propose a new meta-learning based framework termed
MLC for learning with weak supervision. Experiments with different
label noise levels on multiple datasets show that MLC can achieve
large improvement over previous methods incorporating weak labels for
learning.