[CFP] - (R2LM) From Rules to Language Models: Comparative Performance Evaluation @ RANLP 2025 (Varna, Bulgaria) - 11-13 September 2025

https://r2lm2025.github.io/R2LM/

Workshop Description

Deep learning (DL) and large language models (LLMs) have driven major advances in natural language processing (NLP), enabling impressive performance across many tasks. However, they continue to face key challenges in handling complex linguistic phenomena such as multiword expressions, long-context reasoning, and robustness to adversarial inputs. In parallel, concerns remain about the scalability, interpretability, and domain adaptability of these models, particularly in applications requiring high precision, such as grammar checking, legal analysis, or medical NLP. These limitations have sparked renewed interest in rule-based and knowledge-based approaches, which often offer better explainability and remain competitive, especially in low-resource or high-stakes scenarios.

Our workshop aims to gather contributions that deal with the following topics:

• Role of rule-based and knowledge-based NLP methods in modern applications
• Comparative analysis of rule-based, machine-learning, deep-learning and large language models for different NLP tasks
• Emerging trends in NLP research beyond deep learning and Large Language Models
• Limitations and performance bottlenecks in scalability and accuracy of deep learning models

Submission Details

• Long papers: up to 8 pages (excluding references)
• Short papers: up to 4 pages (excluding references)
• Format: ACL-style (LaTeX or MS Word)
• Submission portal and template info available on the RANLP 2025 website

Important dates

Paper Submission Deadline: 6 July 2025
Notification of Acceptance: 31 July 2025
Workshop date: 11, 12 or 13 September 2025

Organising Committee:

Alicia Picazo-Izquierdo, University of Alicante, Spain
Ernesto Luis Estevanell-Valladares, University of Alicante, Spain
Rafael Muñoz Guillena, University of Alicante, Spain
Ruslan Mitkov, Lancaster University, UK
Raúl García Cerdá, University of Alicante, Spain