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 this https URL.

本篇论文提出了一种基于多样性释义的句法丰富分布的鲁棒性训练框架，用于解决模型训练中句法分布现实世界的不完全可观察性挑战。该框架包含两种算法，旨在恢复表达转化的知识。该框架可以应用于其他句法部分可观察的领域。在基于框架构建的验证集 CaRB-AutoPara 中，失败的模型在句法分布差异增加时性能下降，而实验结果表明，我们的框架可以给出一个鲁棒的边界。