About us

The 9th Workshop on Natural Language for Artificial Intelligence (NL4AI 2026) aims to explore the role of Computational Linguistics and Natural Language Processing in AI applications. The workshop facilitates discussions on integrating advanced NLP techniques with wider AI contexts, focusing on theoretical models, applied techniques, and research in progress.

Important Dates

Mon, Jun 29, 2026

Paper Submission deadline

Fri, Jul 31, 2026

Notification to authors

Wed, Aug 26, 2026

Camera-ready due

Tue, Oct 6, 2026

Workshop start date

Fri, Oct 9, 2026

Workshop end date

Paper Submission

Research Paper Submission Tracks

Semantics

NLP and AI Applications (health, legal domain, social media and journalism, etc.)

Natural Language Interfaces for Human Robot Interaction

Resources, Benchmarks, and Evaluation

Discourse and Pragmatics

Natural Language Generation

Creativity, Style, and Narrative Generation

Summarization

Information Extraction in AI Applications

Machine Learning for NLP

LLMs, Foundation Models and Applications

Interpretability, Explainability and Analysis of Models for NLP

Natural Language Inference

Question Answering and Reading Comprehension

Sentiment Analysis, Opinion Mining, and Argumentation

Abusive Language Detection and Analysis

NLP for Fact Checking, Fake News Detection and Analysis

Conversational Agents in Human-Computer Interaction

Speech and Spoken Language Processing

Language and other Multimodality

Multimodal (text-image) data sources

Machine Translation and Multilinguality

Low-Resource NLP and Linguistic Diversity

Cognitive Modeling and Psycholinguistics

Computational Historical Linguistics, Social Science, and Cultural Analytics

Ethics, Fairness, and Societal Impacts of NLP

NLP and Industrial Challenges

Call for Papers Description

We encourage original submissions that describe new theoretical models, applied techniques, and research in progress. We invite short/demo papers (max 6 pages + 2 pages references) and regular papers (max 12 pages + 2 pages references). Submissions must be peer-reviewed and follow the CEUR-ART 1-column style.