论文标题
在法律文本处理中改善细心的神经网络
Toward Improving Attentive Neural Networks in Legal Text Processing
论文作者
论文摘要
近年来，由于神经网络技术的突破，尤其是专门的深度学习模型，自然语言处理已取得了许多令人印象深刻的成就。但是，自动化文字处理仍然是自然语言处理的困难分支。法律判决通常很长，包含复杂的法律术语。因此，在处理法律文件时，在一般文件上运作良好的模型仍然面临挑战。我们在这项工作中的实验中验证了这个问题的存在。在本文中，我们有选择地介绍了在自动法律文档处理中改善细心神经网络的主要成就。但是，语言模型往往会越来越大，但是，如果没有专家知识，这些模型仍然会在域适应中失败，尤其是对于法律等专业领域。
In recent years, thanks to breakthroughs in neural network techniques especially attentive deep learning models, natural language processing has made many impressive achievements. However, automated legal word processing is still a difficult branch of natural language processing. Legal sentences are often long and contain complicated legal terminologies. Hence, models that work well on general documents still face challenges in dealing with legal documents. We have verified the existence of this problem with our experiments in this work. In this dissertation, we selectively present the main achievements in improving attentive neural networks in automatic legal document processing. Language models tend to grow larger and larger, though, without expert knowledge, these models can still fail in domain adaptation, especially for specialized fields like law.
