Transformer-based language models (TLMs) have widely been recognized to be a
cutting-edge technology for the successful development of deep-learning-based
solutions to problems and applications that require natural language processing
and understanding. Like for other textual domains, TLMs have indeed pushed the
state-of-the-art of AI approaches for many tasks of interest in the legal
domain. Despite the first Transformer model being proposed about six years ago,
there has been a rapid progress of this technology at an unprecedented rate,
whereby BERT and related models represent a major reference, also in the legal
domain. This article provides the first systematic overview of TLM-based
methods for AI-driven problems and tasks in the legal sphere. A major goal is
to highlight research advances in this field so as to understand, on the one
hand, how the Transformers have contributed to the success of AI in supporting
legal processes, and on the other hand, what are the current limitations and
opportunities for further research development.

这篇文章给出了 TLM 在法律领域中基于 AI 的问题和任务的系统概述，旨在突出此领域的研究进展，从而理解 Transformer 模型在支持法律流程中的作用、当前的限制和进一步的研究发展机会。

为人工智能和法律领域引入基于 Transformer 的语言模型的秩序

Bringing order into the realm of Transformer-based language models for  artificial intelligence and law

The history of metaphor research also marks the evolution of knowledge
infusion research. With the continued advancement of deep learning techniques
in recent years, the natural language processing community has shown great
interest in applying knowledge to successful results in metaphor recognition
tasks. Although there has been a gradual increase in the number of approaches
involving knowledge injection in the field of metaphor recognition, there is a
lack of a complete review article on knowledge injection based approaches.
Therefore, the goal of this paper is to provide a comprehensive review of
research advances in the application of deep learning for knowledge injection
in metaphor recognition tasks. In this paper, we systematically summarize and
generalize the mainstream knowledge and knowledge injection principles, as well
as review the datasets, evaluation metrics, and benchmark models used in
metaphor recognition tasks. Finally, we explore the current issues facing
knowledge injection methods and provide an outlook on future research
directions.

该研究对深度学习在隐喻识别任务中的知识注入应用进行了全面的综述和总结，包括主流知识和知识注入原则、数据集、评估指标和基准模型，并探讨了当前知识注入方法面临的问题和未来研究方向。

基于深度学习的隐喻检测知识注入：综述

Deep Learning-Based Knowledge Injection for Metaphor Detection: A  Comprehensive Review

Visual analytics systems combine machine learning or other analytic
techniques with interactive data visualization to promote sensemaking and
analytical reasoning. It is through such techniques that people can make sense
of large, complex data. While progress has been made, the tactful combination
of machine learning and data visualization is still under-explored. This
state-of-the-art report presents a summary of the progress that has been made
by highlighting and synthesizing select research advances. Further, it presents
opportunities and challenges to enhance the synergy between machine learning
and visual analytics for impactful future research directions.

该研究综述了视觉分析、机器学习和数据可视化之间的进展，以及他们之间的机遇和挑战，为未来的研究方向提供了帮助。