[A stylized clam.]

Computational
Linguistics
at
Manitoba

About CLAM

The Computational Linguistics at Manitoba (CLAM) Lab advances research at the confluence of artificial intelligence and linguistics:

  • We develop digital tools, methods, and resources that help linguists develop and test linguistic theories.
  • We develop natural language processing systems that help digital humanists and computational social scientists make sense of large, unstructured text collections.
  • We develop interactive language technology that supports the work of writers, translators, and other professional knowledge workers.

Team

CLAM is headed by Dr. Tristan Miller at the University of Manitoba's Department of Computer Science.

The Lab maintains close working relationships with the Austrian Research Institute for Artificial Intelligence (OFAI) and the Semantic Artificial Intelligence and Creativity Laboratory (SAICL) at East Texas A&M University.

Interested in joining our team? We are currently offering funded PhD positions for research topics in computational humour, historical born-digital corpora, and Indigenous language technology.

News

BDCAM25 panel convened

Usenet as an archive of digital discourse

CHum proceedings published

Workshop on computational humour

Can AI create humour?

PunCAT featured on ORF Ö1

Early Career Researcher Day

Opportunities, challenges of AI in research

Canada's Innovation Leaders article

AI brings cultural nuances to humour

UM Linguistics colloquium

Computational processing of wordplay

Austrian public radio interview

Tristan Miller on AI in education

Mawachihitotaak 2024

Métis Studies Symposium at UM

EJHR article on visual humour

Study on role of scale distortion

JOKER Lab at CLEF 2025

Workshop on humour in the machine

Featured publications

Liana Ermakova, Anne-Gwenn Bosser, Tristan Miller, and Ricardo Campos.
CLEF 2025 JOKER lab: Humour in the machine.
In Advances in Information Retrieval: 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6–10, Proceedings, Part V, volume 15576 of Lecture Notes in Computer Science (ISSN 0302-9743), pages 389–397, Cham, April 2025. Springer. ISBN 978-3-031-88719-2. DOI: 10.1007/978-3-031-88720-8_59.
Over the last three years, the JOKER Lab series at CLEF has gathered an active community of researchers in natural language processing and information retrieval to collaborate on non-literal use of language in text. Such language can be a challenge for AI systems, but also sometimes for humans, as it requires understanding implicit cultural references and unorthodox interactions between form and meaning. In this paper, we discuss the lessons learned from the previous iterations of the Lab and describe how its upcoming edition will build upon those to address new challenges. In 2025, JOKER will provide novel tasks and update some previous ones with new data and new languages. This year we provide sandbox environments for experimenting with humour-aware information retrieval (Task 1), a previously featured task now enhanced with an all-new Portuguese corpus; wordplay translation in text (Task 2), another historical task for which we provide new corpora; onomastic wordplay (Task 3), a new task focussed on humorous proper names in fiction; and controlled creativity (Task 4), another novel task that aims at identifying and avoiding hallucinations.
@inproceedings{ermakova2025clef,
author       = {Liana Ermakova and Anne-Gwenn Bosser and Tristan Miller and Ricardo Campos},
title        = {{CLEF} 2025 {JOKER} Lab: Humour in the Machine},
booktitle    = {Advances in Information Retrieval: 47th {European} {Conference} on {Information} {Retrieval}, {ECIR} 2025, {Lucca}, {Italy}, {April} 6–10, Proceedings, Part {V}},
volume       = {15576},
pages        = {389--397},
series       = {Lecture Notes in Computer Science},
month        = apr,
year         = {2025},
publisher    = {Springer},
address      = {Cham},
isbn         = {978-3-031-88719-2},
issn         = {0302-9743},
doi          = {10.1007/978-3-031-88720-8_59},
}

Steffen Eger, Yong Cao, Jennifer D'Souza, Andreas Geiger, Christian Greisinger, Stephanie Gross, Yufang Hou, Brigitte Krenn, Anne Lauscher, Yizhi Li, Chenghua Lin, Nafise Sadat Moosavi, Wei Zhao, and Tristan Miller.
Transforming science with large language models: a survey on AI-assisted scientific discovery, experimentation, content generation, and evaluation.
ArXiv e-prints, 2502.05151, February 2025. DOI: 10.48550/arXiv.2502.05151.
With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. Recently, a plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. This includes all aspects of the research cycle, especially (1) searching for relevant literature; (2) generating research ideas and conducting experimentation; generating (3) text-based and (4) multimodal content (e.g., scientific figures and diagrams); and (5) AI-based automatic peer review. In this survey, we provide an in-depth overview over these exciting recent developments, which promise to fundamentally alter the scientific research process for good. Our survey covers the five aspects outlined above, indicating relevant datasets, methods and results (including evaluation) as well as limitations and scope for future research. Ethical concerns regarding shortcomings of these tools and potential for misuse (fake science, plagiarism, harms to research integrity) take a particularly prominent place in our discussion. We hope that our survey will not only become a reference guide for newcomers to the field but also a catalyst for new AI-based initiatives in the area of “AI4Science”.
@article{eger2025transforming,
author       = {Steffen Eger and Yong Cao and Jennifer D'Souza and Andreas Geiger and Christian Greisinger and Stephanie Gross and Yufang Hou and Brigitte Krenn and Anne Lauscher and Yizhi Li and Chenghua Lin and Nafise Sadat Moosavi and Wei Zhao and Tristan Miller},
title        = {Transforming Science with Large Language Models: a Survey on {AI}-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation},
journal      = {{ArXiv} e-prints},
volume       = {2502.05151},
month        = feb,
year         = {2025},
doi          = {10.48550/arXiv.2502.05151},
}

Christian F. Hempelmann, Julia Rayz, Tiansi Dong, and Tristan Miller, editors.
Proceedings of the 1st Workshop on Computational Humor (CHum).
Association for Computational Linguistics, Kerville, TX, January 2025. ISBN 979-8-89176-204-6.
@book{hempelmann2025first,
editor       = {Christian F. Hempelmann and Julia Rayz and Tiansi Dong and Tristan Miller},
title        = {Proceedings of the 1st Workshop on Computational Humor ({CHum})},
month        = jan,
year         = {2025},
publisher    = {Association for Computational Linguistics},
address      = {Kerville, TX},
isbn         = {979-8-89176-204-6},
}

In contrast to verbal humour, visual humour remains a relatively underdeveloped area of research. In this exploratory study, we investigate whether scale incongruity – i.e., discrepancy between the expected and actual experience of the size of an object – can serve as a source of humour in the visual modality. We adapt a pre-existing visual data set of mundane scenes by altering the size of an individual object in each scene and collecting humorousness ratings from human annotators on the original and scale-distorted versions. Our analysis of these annotations reveals that scenes with distorted objects are perceived to be significantly funnier than the original images.
@article{swaboda2024use,
author       = {Clara Swaboda and Tristan Miller},
title        = {On the Use of Scale Distortion for Visual Humour: {A} Preliminary Analysis},
journal      = {European Journal of Humour Research},
volume       = {12},
number       = {2},
pages        = {206--211},
month        = jun,
year         = {2024},
issn         = {2307-700X},
doi          = {10.7592/EJHR.2024.12.2.904},
}