面向保险纠纷的韩国法律判决预测数据集
A Korean Legal Judgment Prediction Dataset for Insurance Disputes
AI总结：
本文构建了韩国保险纠纷法律判决预测数据集，针对低资源场景数据稀缺问题，验证了SetFit微调方法在数据有限时可达到与基准模型相近的性能。
摘要：
本文介绍了一个面向保险纠纷的韩国法律判决预测（LJP）数据集。性能优异的保险纠纷LJP模型可惠及保险公司及其客户，能让双方预判进入纠纷调解程序后的结果，从而节省时间与金钱。与低资源语言的常见情况一致，该特定任务的可用数据量存在限制。为缓解这一问题，本文探究了在数据有限的条件下如何取得良好性能。实验中，我们证明当训练数据有限时，句子Transformer微调（SetFit，Tunstall等人，2022）是标准微调的优质替代方案。尽管数据规模小得多，在我们的数据集上用SetFit方法微调的模型，性能与韩国LJP基准模型（Hwang等人，2022）相近。
英文摘要：
This paper introduces a Korean legal judgment prediction (LJP) dataset for insurance disputes. Successful LJP models on insurance disputes can benefit insurance companies and their customers. It can save both sides' time and money by allowing them to predict how the result would come out if they proceed to the dispute mediation process. As is often the case with low-resource languages, there is a limitation on the amount of data available for this specific task. To mitigate this issue, we investigate how one can achieve a good performance despite the limitation in data. In our experiment, we demonstrate that Sentence Transformer Fine-tuning (SetFit, Tunstall et al., 2022) is a good alternative to standard fine-tuning when training data are limited. The models fine-tuned with the SetFit approach on our data show similar performance to the Korean LJP benchmark models (Hwang et al., 2022) despite the much smaller data size.
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。
