The disclosed embodiments include a method to predict annotation spans without requiring any labeled annotation data. The approach is to consider AES as a Multiple Instance Learning (MIL) task. The disclosed embodiments show that such models can both predict content scores and localize content by leveraging their sentence-level score predictions. This capability arises despite never having access to annotation training data. Implications are discussed for improving formative feedback and explainable AES models.
MULTIPLE INSTANCE LEARNING FOR CONTENT FEEDBACK LOCALIZATION WITHOUT ANNOTATION