Syntactically Robust Training on Partially-Observed Data for Open Information Extraction

Understand

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.