Efficient Data Learning for Open Information Extraction with Pre-trained Language Models

arXiv:2310.15021v2 Announce Type: replace
Abstract: 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 …

abstract advantages arxiv cs.ai cs.cl data extraction fundamental generate information information extraction labeling language language models language processing natural natural language natural language processing object processing replace tokens type unique while