Legal judgment Prediction (LJP), aiming to predict a judgment based on fact
descriptions, serves as legal assistance to mitigate the great work burden of
limited legal practitioners. Most existing methods apply various large-scale
pre-trained language models (PLMs) finetuned in LJP tasks to obtain consistent
improvements. However, we discover the fact that the state-of-the-art (SOTA)
model makes judgment predictions according to wrong (or non-casual)
information, which not only weakens the model's generalization capability but
also results in severe social problems like discrimination. Here, we analyze
the causal mechanism misleading the LJP model to learn the spurious
correlations, and then propose a framework to guide the model to learn the
underlying causality knowledge in the legal texts. Specifically, we first
perform open information extraction (OIE) to refine the text having a high
proportion of causal information, according to which we generate a new set of
data. Then, we design a model learning the weights of the refined data and the
raw data for LJP model training. The extensive experimental results show that
our model is more generalizable and robust than the baselines and achieves a
new SOTA performance on two commonly used legal-specific datasets.

本文研究法律判决预测（LJP），提出了一种基于因果知识的框架，用于引导模型从法律文本中学习潜在的因果关系，通过开放式信息提取（OIE）筛选出高比例的因果信息，并使用模型学习精炼数据的权重和原始数据，使模型具有更好的泛化性和鲁棒性。经过广泛的实验验证，该模型比基线模型更多样化且在两个常用的法律特定数据集上实现了新的 SOTA 性能。

知识即力量：理解因果关系使法律判断预测模型更具普适性和健壮性

Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust

Causality knowledge is crucial for many artificial intelligence systems.
Conventional textual-based causality knowledge acquisition methods typically
require laborious and expensive human annotations. As a result, their scale is
often limited. Moreover, as no context is provided during the annotation, the
resulting causality knowledge records (e.g., ConceptNet) typically do not take
the context into consideration. To explore a more scalable way of acquiring
causality knowledge, in this paper, we jump out of the textual domain and
investigate the possibility of learning contextual causality from the visual
signal. Compared with pure text-based approaches, learning causality from the
visual signal has the following advantages: (1) Causality knowledge belongs to
the commonsense knowledge, which is rarely expressed in the text but rich in
videos; (2) Most events in the video are naturally time-ordered, which provides
a rich resource for us to mine causality knowledge from; (3) All the objects in
the video can be used as context to study the contextual property of causal
relations. In detail, we first propose a high-quality dataset Vis-Causal and
then conduct experiments to demonstrate that with good language and visual
representation models as well as enough training signals, it is possible to
automatically discover meaningful causal knowledge from the videos. Further
analysis also shows that the contextual property of causal relations indeed
exists, taking which into consideration might be crucial if we want to use the
causality knowledge in real applications, and the visual signal could serve as
a good resource for learning such contextual causality.

本文探讨了从视觉信号中学习上下文因果关系的可能性，提出了高质量数据集 Vis-Causal，并展示了通过好的语言和视觉表征模型和充足的训练信号，可以从视频中自动发现有意义的因果知识。