  Conventional Open Information Extraction (Open IE) systems are usually built
on hand-crafted patterns from other NLP tools such as syntactic parsing, yet
they face problems of error propagation. In this paper, we propose a neural
Open IE approach with an encoder-decoder framework. Distinct from existing
methods, the neural Open IE approach learns highly confident arguments and
relation tuples bootstrapped from a state-of-the-art Open IE system. An
empirical study on a large benchmark dataset shows that the neural Open IE
system significantly outperforms several baselines, while maintaining
comparable computational efficiency.
