DGGCCM: a hybrid neural model for legal event detection

Abstract

This paper introduces an advanced event detection model for legal intelligence, focusing on identifying event types in legal cases by examining trigger word candidates. It employs the DeBERTa pre-trained language model for encoding sentences into enriched word representations, supplemented by the Global Pointer neural network for initial scoring. The model further uses a graph convolutional network, conditional layer normalisation, and a convolutional neural network to extract features from these representations. A multilayer perceptron then determines the event type based on these features and initial scores. Additionally, a dictionary-matching method revises the predicted event types, with adversarial training and a sentence-length mask employed to enhance model performance and address missing trigger words. The model’s effectiveness is proven through extensive experimentation, outperforming state-of-the-art baselines (including some large language models) and securing third prize in the event detection task at the Challenge of AI in Law (CAIL) 2022. The code of our model is available at https://github.com/1gst/DGGCCN/tree/main.