Passage Ranking with Weak Supervision
In this paper, we propose a framework for neural ranking tasks based on the data programming paradigm p, which enables us to leverage multiple weak supervision signals from different sources.Empirically, we consider two sources of weak supervision signals, unsupervised ranking functions and semantic feature similarities.We train a BERT-based passage-ranking model in our weak supervision framework.Without using ground-truth training labels, BERT-PR models outperform BM25 baseline by a large margin on all three datasets and even beat the previous state-of-the-art results with full supervision on two of datasets.
We propose a weak supervision training pipeline based on the data programming framework for ranking tasks, in which we train a BERT-base ranking model and establish the new SOTA.The authors propose a combination of BERT and the weak supervision framework to tackle the problem of passage ranking, obtaining results better than the fully supervised state-of-the-art.