  One of the biggest bottlenecks in building accurate, high coverage neural
open IE systems is the need for large labelled corpora. The diversity of open
domain corpora and the variety of natural language expressions further
exacerbate this problem. In this paper, we propose a syntactic and
semantic-driven learning approach, which can learn neural open IE models
without any human-labelled data by leveraging syntactic and semantic knowledge
as noisier, higher-level supervisions. Specifically, we first employ syntactic
patterns as data labelling functions and pretrain a base model using the
generated labels. Then we propose a syntactic and semantic-driven reinforcement
learning algorithm, which can effectively generalize the base model to open
situations with high accuracy. Experimental results show that our approach
significantly outperforms the supervised counterparts, and can even achieve
competitive performance to supervised state-of-the-art (SoA) model
