The AMELR workshop focuses on Legal Argument Mining (LAM) - using NLP to automatically detect legal arguments. Recent developments in NLP and LAM have provided legal scholars with a powerful tool for studying reasoning patterns, interpretative theories, and biases across jurisdictions and legal systems. The workshop gathers experts in computer science, AI & Law, legal theory, and empirical legal studies to address key challenges of LAM: creating training datasets, developing reliable models, establishing reproducibility standards, and integrating LAM into legal research. The workshop aims to strengthen the emerging field of LAM and its role in empirical legal studies by sharing latest implementations, addressing core challenges, and establishing best practices.

Argument mining (also known as “argumentation mining”) is a well-established research area in computational linguistics that focuses on the automatic identification of argumentative structures, such as premises, conclusions, and inference schemes. Since its beginnings, the focus has been on the development of large-scale argumentation dataset and tasks like argument quality assessment, argument persuasiveness, and the synthesis of argumentative texts, spanning various domains, such as legal, social, medical, political, and scientific settings.

The ASAIL workshop series and interest group serves as a platform for researchers and practitioners working on natural language processing of legal text. Its goals include (i) Organising regular peer-reviewed workshop events for presentation and discussion of research and practical implementations around legal NLP; (ii) Facilitating communication and collaboration among academic researchers as well as practitioners from industry, government, and the public sector, and other interested individuals and organisations; (iii) Providing an entry point into the research field and community.

The “Language Understanding in the Human-Machine Era” (LUHME) workshop aims to reignite, retrieve, resume, and refocus the enduring debate about the role of understanding in natural language use and related applications. Specifically, it seeks to elucidate the nature of language understanding and ascertain whether it is indispensable for computational natural language tasks such as automated translation and natural language generation. Furthermore, it aims to provide insight into the role played by language professionals (e.g., linguists, professional translators, interpreters, language educators) in computational natural language understanding. It will, therefore, convene researchers interested in the intersection of language understanding and the effective use of language technologies in human-machine interaction.