面向合同信息抽取的动态多任务学习方法

Accurately extracting two types of information including elements and clauses in contract texts can effectively improve the contract review efficiency and provide facilitation services for all trading parties. However, current contract information extraction methods generally train single-task models to extract elements and clauses separately, whereas they do not dig deep into the characteristics of contract texts, ignoring the relevance among different tasks. Therefore, this study employs a deep neural network structure to study the correlation between the two tasks of element extraction and clause extraction and proposes a multitask learning method. Firstly, the primary multitask learning model is built for contract information extraction by combining the above two tasks. Then, the model is optimized and attention mechanism is adopted to further explore the correlation. Additionally, an Attention-based dynamic multitask-learning model is built. Finally, based on the above two methods, adynamic multitask learning model with lexical knowledge is proposed for the complex semantic environment in contract texts. The experimental results show that the method can fully capture the shared features among tasks and yield better information extraction results than the single-task model. It can solve the nested entity among elements and clauses in contract texts, and realize the joint information extraction of contract elements and clauses. In addition, to verify the robustness of the proposed method, this study conducts experiments on public datasets in various fields, and the results show that the proposed method is superior to baseline methods.