Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search.
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs.
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings.
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks.
In this paper, we revisit the end-to-end triple extraction task for sequence generation.
Ranked #6 on Relation Extraction on NYT