Deep Bayesian Active Learning for Learning to Rank: A Case Study in Answer Selection (Extended Abstract)

Qunbo Wang, Wenjun Wu, Yuxing Qi, Yongchi Zhao

Abstract: Active learning can select informative data for model training to reduce the amount of labelling efforts required. Because traditional active learning methods cannot be directly used for deep learning, researchers have proposed multiple deep active learning methods. However, none of the previous research efforts on deep active learning algorithms presents a specific framework for learning-to-rank tasks. In this work, we introduce a novel deep active learning framework based on Deep Expected Loss Optimization (DELO) for the answer selection task.

External IDs:dblp:conf/icde/Wang0QZ23