A Light Ranker for Open-Domain Question Answering

Open-domain question answering aims to extract answers from some unlabeled texts. Existing approaches usually follow the retrieve-then-read pipeline, which depends heavily on the quality of the retriever. Therefore, a ranker is significant to denoise those irrelevant paragraphs by giving each candidate paragraph a confidence score. In this paper, we design a light paragraph ranker, which utilizes an iterative approach to combine the paragraph-paragraph relevance and the paragraph-question relevance. Our ranker can fully mine global paragraph information and is lighter than that in most other Open-domain QA models. Furthermore, we explore two types of readers based on whether they sample candidate paragraphs or retain all. Experiments on public datasets show that our model achieves improvements compared to baselines.