English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Transformer-based language models (TLMs) have widely been recognized to be acutting-edge technology for the successful development of deep-learning-basedsolutions to problems and applications that require natural language processingand understanding. Like for other textual domains, TLMs have indeed pushed thestate-of-the-art of AI approaches for many tasks of interest in the legaldomain. 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 legaldomain. This article provides the first systematic overview of TLM-basedmethods for AI-driven problems and tasks in the legal sphere. A major goal isto highlight research advances in this field so as to understand, on the onehand, how the Transformers have contributed to the success of AI in supportinglegal processes, and on the other hand, what are the current limitations andopportunities for further research development.

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