  Open Information Extraction models have shown promising results with
sufficient supervision. However, these models face a fundamental challenge that
the syntactic distribution of training data is partially observable in
comparison to the real world. In this paper, we propose a syntactically robust
training framework that enables models to be trained on a syntactic-abundant
distribution based on diverse paraphrase generation. To tackle the intrinsic
problem of knowledge deformation of paraphrasing, two algorithms based on
semantic similarity matching and syntactic tree walking are used to restore the
expressionally transformed knowledge. The training framework can be generally
applied to other syntactic partial observable domains. Based on the proposed
framework, we build a new evaluation set called CaRB-AutoPara, a syntactically
diverse dataset consistent with the real-world setting for validating the
robustness of the models. Experiments including a thorough analysis show that
the performance of the model degrades with the increase of the difference in
syntactic distribution, while our framework gives a robust boundary. The source
code is publicly available at https://github.com/qijimrc/RobustOIE.
