MULTIPLE INSTANCE LEARNING FOR CONTENT FEEDBACK LOCALIZATION WITHOUT ANNOTATION

Abstract

The disclosed embodiments may include a method to predict annotation spans without requiring any labeled annotation data. The approach may consider AES as a Multiple Instance Learning (MIL) task. The disclosed embodiments may show that such models can both predict content scores and localize content by leveraging their sentence-level score predictions. This capability may arise despite never having access to annotation training data. Implications may be discussed for improving formative feedback and explainable AES models.

CPC Classifications