ReInfoSelect

2000년 도입 · 논문 1편에서 사용

ReInfoSelect is a reinforcement weak supervision selection method for information retrieval. It learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward. Iteratively, for a batch of anchor-document pairs, ReInfoSelect back propagates the gradients through the neural ranker, gathers its NDCG reward, and optimizes the data selection network using policy gradients, until the neural ranker's performance peaks on target relevance metrics (convergence).

출처: Selective Weak Supervision for Neural Information Retrieval

소개 논문: Selective Weak Supervision for Neural Information Retrieval