Short papers
Up to 4 pages, excluding references, with 1 additional page allowed for camera-ready version to address reviewer comments.
NLP/CORE Unranked
CDL 2026
Workshop on Computational Developmental Linguistics
The 1st Workshop on Computational Developmental Linguistics (CDL) 2026, co-located with ACL 2026 in San Diego, brings together researchers from machine learning, computational linguistics, cognitive science, and developmental psychology to study computational models of language acquisition and change in both humans and machines. The workshop aims to foster rigorous cross-disciplinary comparisons between human and artificial language learning, exploring developmental trajectories, learning dynamics in neural language models, and applications in education and clinical NLP.
Key deadlines
Verified September 9, 2026Full paper
March 21, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
March 21, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Full papers
Up to 8 pages, excluding references, with 1 additional page allowed for camera-ready version to address reviewer comments.
Research areas in scope
01
Topics of Interest
Computational models for developmental linguisticsModels and formalisms for simulating first and second language acquisition, including data constraints, model architectures, training objectives, and frameworks for modeling idiolect change and semantic driftLearning dynamics in pretraining and post-training of LMsBehavioral and mechanistic interpretations of how linguistic and cognitive competence emerge during pretraining, and how it evolves through post-training (e.g., finetuning, alignment, and domain adaptation)In addition to text-only LMs, we encourage approaches that integrate multiple modalities and interactive learning, as these more closely mirror human language acquisition processesComparisons of developmental linguistics in humans and machinesComparing the processes, constraints, and outcomes of language acquisition in humans and artificial learners; examining the possibilities, limitations, and methodological challenges of such comparisons; relating model learning trajectories to developmental timelines and milestones in humans; and conducting these analyses in a scientifically rigorous manner without over-anthropomorphizing LMsKnowledge tracing and developmental diagnosticsDeveloping tools and methods to track the acquisition, transformation, and retention of linguistic knowledge in LMs, including phase transition detection, benchmarks, and probes of representational changeApplications in language education and clinical NLPApplying insights from computational developmental linguistics to improve LM engineering, language tutoring systems, child-directed AI interaction, adaptive educational technology, and computational models of neurological communication disorders
Policies worth checking twice
- Submissions must be prepared in PDF format using the ACL-style template through OpenReview
- Authors must indicate whether their submission is 'archival' or 'non-archival' at the OpenReview portal
- Archival option does not influence acceptance decision
- Papers on arXiv or under review elsewhere are allowed if marked as non-archival
- Papers accepted to ACL 2026 main or findings track can be submitted to CDL if marked as non-archival
- Submissions exceeding 4 pages (excluding references) may not be suitable for submission to computer vision conferences like CVPR/ICCV due to their publication policies
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026