  Answering complex open-domain questions requires understanding the latent
relations between involving entities. However, we found that the existing QA
datasets are extremely imbalanced in some types of relations, which hurts the
generalization performance over questions with long-tail relations. To remedy
this problem, in this paper, we propose a Relation-Guided Pre-Training
(RGPT-QA) framework. We first generate a relational QA dataset covering a wide
range of relations from both the Wikidata triplets and Wikipedia hyperlinks. We
then pre-train a QA model to infer the latent relations from the question, and
then conduct extractive QA to get the target answer entity. We demonstrate that
by pretraining with propoed RGPT-QA techique, the popular open-domain QA model,
Dense Passage Retriever (DPR), achieves 2.2%, 2.4%, and 6.3% absolute
improvement in Exact Match accuracy on Natural Questions, TriviaQA, and
WebQuestions. Particularly, we show that RGPT-QA improves significantly on
questions with long-tail relations
