Toward Improving Attentive Neural Networks in Legal Text Processing
This work addresses automated legal document processing, a difficult domain-specific problem, but appears incremental as it builds on existing attentive models.
The paper tackles the challenge of applying attentive neural networks to legal text processing, where long sentences and complex terminology hinder performance, and presents improvements through domain adaptation.
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.