基于 Transformer 的模型在裁判文书分类上的比较研究
Comparative study on Judgment Text Classification for Transformer Based Models
AI总结：
本文比较 6 种自注意力 Transformer 模型在 4 种激活函数下的裁判文书分类与胜诉方预测性能，模型经 200 个裁判上下文训练，预测置信度最高达 99%。
摘要：
本工作涉及使用多种 NLP 模型，通过从裁判文书中进行文本抽取和摘要来预测某一裁判的胜诉方。这些文书在法律程序中非常有用。其优势之一是可在诉讼和案件中用于引用和判例参考，使使用者能够为其案件构建有力论证。谈到判例时，有必要查阅大量文书，以收集与案件相关的法律要点。然而，由于复杂的用词结构和文书篇幅，审查这些文书需要很长时间进行分析。本工作对 6 种不同的基于自注意力机制的 Transformer 模型进行了比较研究，并考察它们在采用 4 种不同激活函数进行调整时的性能表现。这些模型使用 200 个裁判上下文进行训练，并依据不同的基准参数评估其结果。这些模型在预测裁判结果时最终可达到高达 99% 的置信度。这可用于获取特定裁判文书，而无需花费过多时间搜索相关案件并完整阅读它们。
英文摘要：
This work involves the usage of various NLP models to predict the winner of a particular judgment by the means of text extraction and summarization from a judgment document. These documents are useful when it comes to legal proceedings. One such advantage is that these can be used for citations and precedence reference in Lawsuits and cases which makes a strong argument for their case by the ones using it. When it comes to precedence, it is necessary to refer to an ample number of documents in order to collect legal points with respect to the case. However, reviewing these documents takes a long time to analyze due to the complex word structure and the size of the document. This work involves the comparative study of 6 different self-attention-based transformer models and how they perform when they are being tweaked in 4 different activation functions. These models which are trained with 200 judgement contexts and their results are being judged based on different benchmark parameters. These models finally have a confidence level up to 99% while predicting the judgment. This can be used to get a particular judgment document without spending too much time searching relevant cases and reading them completely.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
