Efficient Data Learning for Open Information Extraction with Pre-trained
  Language Models
Zhiyuan Fan Shizhu He
Open Information Extraction (OpenIE) is a fundamental yet challenging task in
Natural Language Processing, which involves extracting all triples (subject,
predicate, object) from a given sentence. While labeling-based methods have
their merits, generation-based techniques offer unique advantages, such as the
ability to generate tokens not present in the original sentence. However, these
generation-based methods often require a significant amount of training data to
learn the task form of OpenIE and substantial training time to overcome slow
model convergence due to the order penalty. In this paper, we introduce a novel
framework, OK-IE, that ingeniously transforms the task form of OpenIE into the
pre-training task form of the T5 model, thereby reducing the need for extensive
training data. Furthermore, we introduce an innovative concept of Anchor to
control the sequence of model outputs, effectively eliminating the impact of
order penalty on model convergence and significantly reducing training time.
Experimental results indicate that, compared to previous SOTA methods, OK-IE
requires only 1/100 of the training data (900 instances) and 1/120 of the
training time (3 minutes) to achieve comparable results.