Efficient Data Learning for Open Information Extraction with Pre-trained Language Models
This addresses efficiency issues in OpenIE for NLP practitioners by offering a more data- and time-efficient solution, though it is incremental as it builds on existing generation-based methods.
The paper tackles the challenge of Open Information Extraction by introducing OK-IE, a framework that reduces training data needs to 1/100 (900 instances) and training time to 1/120 (3 minutes) compared to previous state-of-the-art methods while achieving comparable results.
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.