Portrait de Ayla Rigouts Terryn

Ayla Rigouts Terryn

Membre académique associé
Professeure adjointe, Université de Montréal, Linguistique et de traduction
Sujets de recherche
Modèles génératifs
Recherche d'information
Traitement du langage naturel
Google Scholar
LinkedIn

Biographie

Ayla Rigouts Terryn est professeure adjointe en technologies de la traduction et en IA au département de linguistique et de traduction de l'Université de Montréal et membre académique associée à Mila. Elle est également chercheuse dans le domaine du traitement du langage naturel (NLP).

Elle a obtenu une maîtrise en traduction à l'université d'Anvers, un doctorat sur l'extraction automatique de terminologie à l'université de Gand et s'est spécialisée dans les technologies linguistiques multilingues en tant que chercheuse principale à la KU Leuven. En tant que linguiste dans le domaine de l'intelligence artificielle, elle se passionne pour l'amélioration de notre compréhension des modèles de langage grâce à des connaissances linguistiques. Elle se concentre principalement sur l'amélioration des technologies dans des contextes multilingues et/ou spécifiques à un domaine.

Publications

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… (voir plus) 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 (prépublication)
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… (voir plus) 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 (prépublication)
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-… (voir plus)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 (publié)
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