In this paper, we study the problem of legal domain adaptation problem from
an imbalanced source domain to a partial target domain. The task aims to
improve legal judgment predictions for non-professional fact descriptions. We
formulate this task as a partial-and-imbalanced domain adaptation problem.
Though deep domain adaptation has achieved cutting-edge performance in many
unsupervised domain adaptation tasks. However, due to the negative transfer of
samples in non-shared classes, it is hard for current domain adaptation model
to solve the partial-and-imbalanced transfer problem. In this work, we explore
large-scale non-shared but related classes data in the source domain with a
hierarchy weighting adaptation to tackle this limitation. We propose to embed a
novel pArtial Imbalanced Domain Adaptation technique (AIDA) in the deep
learning model, which can jointly borrow sibling knowledge from non-shared
classes to shared classes in the source domain and further transfer the shared
classes knowledge from the source domain to the target domain. Experimental
results show that our model outperforms the state-of-the-art algorithms.

本文研究了从不平衡的源领域到部分目标领域的法律领域适应性问题，该领域的任务是提高针对非专业事实描述的法律判断预测。我们将此任务描述为部分和不平衡的领域适应问题，并探索了在源域中大规模的非共享类相关数据，通过分级加权适应来解决这个限制。我们在深度学习模型中嵌入了一种新的部分不平衡领域适应技术（AIDA），它可以共同借用非共享类的兄弟知识来处理源领域中的共享类并进一步将共享类的知识从源领域转移到目标领域。实验结果表明，我们的模型优于现有的算法。

通过部分和失衡的领域适应，为非专业事实描述进行法律判断预测

AIDA: Legal Judgment Predictions for Non-Professional Fact Descriptions via Partial-and-Imbalanced Domain Adaptation

Seeking legal advice is often expensive. Recent advancements in machine
learning for solving complex problems can be leveraged to help make legal
services more accessible to the public. However, real-life applications
encounter significant challenges. State-of-the-art language models are growing
increasingly large, making parameter-efficient learning increasingly important.
Unfortunately, parameter-efficient methods perform poorly with small amounts of
data, which are common in the legal domain (where data labelling costs are
high). To address these challenges, we propose parameter-efficient legal domain
adaptation, which uses vast unsupervised legal data from public legal forums to
perform legal pre-training. This method exceeds or matches the fewshot
performance of existing models such as LEGAL-BERT on various legal tasks while
tuning only approximately 0.1% of model parameters. Additionally, we show that
our method can achieve calibration comparable to existing methods across
several tasks. To the best of our knowledge, this work is among the first to
explore parameter-efficient methods of tuning language models in the legal
domain.

本文提出了一种参数高效的法律领域适应方法，该方法使用公共法律论坛的海量无监督法律数据进行法律预训练，成功地在各种法律任务上超过或匹配现有模型的低样本学习性能，同时只调整了约 0.1％的模型参数，达到与现有方法相当的校准效果。