Portrait of Ayla Rigouts Terryn

Ayla Rigouts Terryn

Associate Academic Member
Assistant Professor, Université de Montréal, Linguistics and translation
Research Topics
Information Retrieval
Large Language Models (LLM)
Linguistic Evaluation of Language Models
Machine Translation
Natural Language Processing
Terminology
Website
Google Scholar
LinkedIn

Biography

Ayla Rigouts Terryn is an Assistant Professor of Translation Technologies and AI in the Department of Linguistics and Translation at Université de Montréal and an Associate Academic Member at Mila. She is also an IVADO professor (regroupement 3: natural language processing (NLP)) and holds the IVADO-FRQ chair "At the Crossroads of Languages and AI: Towards a Synergy Between Language Expertise and Computational Innovation".

She obtained a Master’s degree in translation from Antwerp University, obtained her PhD on automatic terminology extraction from Ghent University, and specialised in multilingual language technology as a senior researcher at KU Leuven. As a linguist in the field of AI, she is passionate about advancing our understanding of language models with linguistic insight. She explores NLP in multilingual and non-English contexts (including low-resource scenarios), nuanced analysis and evaluation of large language models, and translation technology for domain-specific texts and terminology.

Publications

Introduction to the special issue on Computational Terminology
Ayla Rigouts Terryn
Patrick Drouin
2025-05-23
Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication (published)
doi.org
Proceedings of the 18th Workshop on Building and Using Comparable Corpora (BUCC)
Serge Sharoff
Ayla Rigouts Terryn
Pierre Zweigenbaum
Reinhard Rapp
2025-01-01
COLING Workshops (published)
dblp.uni-trier.de
THInC: A Theory-Driven Framework for Computational Humor Detection
Victor De Marez
Thomas Winters
Ayla Rigouts Terryn
Humor is a fundamental aspect of human communication and cognition, as it plays a crucial role in social engagement. Although theories about… (see more) humor have evolved over centuries, there is still no agreement on a single, comprehensive humor theory. Likewise, computationally recognizing humor remains a significant challenge despite recent advances in large language models. Moreover, most computational approaches to detecting humor are not based on existing humor theories. This paper contributes to bridging this long-standing gap between humor theory research and computational humor detection by creating an interpretable framework for humor classification, grounded in multiple humor theories, called THInC (Theory-driven Humor Interpretation and Classification). THInC ensembles interpretable GA2M classifiers, each representing a different humor theory. We engineered a transparent flow to actively create proxy features that quantitatively reflect different aspects of theories. An implementation of this framework achieves an F1 score of 0.85. The associative interpretability of the framework enables analysis of proxy efficacy, alignment of joke features with theories, and identification of globally contributing features. This paper marks a pioneering effort in creating a humor detection framework that is informed by diverse humor theories and offers a foundation for future advancements in theory-driven humor classification. It also serves as a first step in automatically comparing humor theories in a quantitative manner.
2024-09-02
ArXiv (preprint)
doi.org
arxiv.org
THInC: A Theory-Driven Framework for Computational Humor Detection
Victor De Marez
Thomas Winters
Ayla Rigouts Terryn
Humor is a fundamental aspect of human communication and cognition, as it plays a crucial role in social engagement. Although theories about… (see more) humor have evolved over centuries, there is still no agreement on a single, comprehensive humor theory. Likewise, computationally recognizing humor remains a significant challenge despite recent advances in large language models. Moreover, most computational approaches to detecting humor are not based on existing humor theories. This paper contributes to bridging this long-standing gap between humor theory research and computational humor detection by creating an interpretable framework for humor classification, grounded in multiple humor theories, called THInC (Theory-driven Humor Interpretation and Classification). THInC ensembles interpretable GA2M classifiers, each representing a different humor theory. We engineered a transparent flow to actively create proxy features that quantitatively reflect different aspects of theories. An implementation of this framework achieves an F1 score of 0.85. The associative interpretability of the framework enables analysis of proxy efficacy, alignment of joke features with theories, and identification of globally contributing features. This paper marks a pioneering effort in creating a humor detection framework that is informed by diverse humor theories and offers a foundation for future advancements in theory-driven humor classification. It also serves as a first step in automatically comparing humor theories in a quantitative manner.
2024-09-02
ArXiv (preprint)
doi.org
arxiv.org
Exploratory Study on the Impact of English Bias of Generative Large Language Models in Dutch and French
Ayla Rigouts Terryn
Miryam de Lhoneux
The most widely used LLMs like GPT4 and Llama 2 are trained on large amounts of data, mostly in English but are still able to deal with non-… (see more)English languages. This English bias leads to lower performance in other languages, especially low-resource ones. This paper studies the linguistic quality of LLMs in two non-English high-resource languages: Dutch and French, with a focus on the influence of English. We first construct a comparable corpus of text generated by humans versus LLMs (GPT-4, Zephyr, and GEITje) in the news domain. We proceed to annotate linguistic issues in the LLM-generated texts, obtaining high inter-annotator agreement, and analyse these annotated issues. We find a substantial influence of English for all models under all conditions: on average, 16% of all annotations of linguistic errors or peculiarities had a clear link to English. Fine-tuning a LLM to a target language (GEITje is fine-tuned on Dutch) reduces the number of linguistic issues and probably also the influence of English. We further find that using a more elaborate prompt leads to linguistically better results than a concise prompt. Finally, increasing the temperature for one of the models leads to lower linguistic quality but does not alter the influence of English.
2024-01-01
HUMEVAL (published)
www.semanticscholar.org
Exploratory Study on the Impact of English Bias of Generative Large Language Models in Dutch and French
Ayla Rigouts Terryn
Miryam de Lhoneux