Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite

Understand

Label error is a ubiquitous problem in annotated data.

• Large amounts of label error substantially degrades the quality of deep learning models.
• Existing methods to tackle the label error problem largely focus on the classification task, and either rely on task specific architecture or require non-trivial additional computations, which is undesirable or even unattainable for industry usage.
• In this paper, we propose LEDO: a model-agnostic and computationally efficient framework for Label Error Detection and Overwrite.