Emotions in the bilingual mind: Verbalization and detection of emotions in L2.
Abstract
Emotions have been extensively studied in psycholinguistic research on bilingualism, with evidence suggesting that they are processed differently in a native language (L1) than in a non-native language (L2). However, research on how emotions are verbalized in L1 versus L2 written production is lacking. At the same time, research on automatic emotion detection is growing in the fields of computational linguistics and natural language processing, as are studies on cultural awareness of emotion expression in large language models (LLMs). Multilingual models have been shown to exhibit biases towards English and limited sensitivity to the cultural dependence of emotions. Still, little is known about how emotion expression by L2-speakers relates to the performance and biases of language models. In this project, we address these gaps by curating a corpus of L1-L2 narratives from several European languages and analyzing how bilinguals express emotions across language combinations. We will further evaluate multilingual LLMs on emotion generation and detection tasks for L1 versus L2 texts, probing biases related to (i) the native versus non-native status of the text (L1 vs. L2), and (ii) the specific languages involved. The research will provide new insights into the psycholinguistic study of emotions in bilinguals, and also inform the development of more inclusive and emotion-sensitive language technologies that better reflect the linguistic realities of L2 speakers.Researcher(s)
- Promoter: De Bruyne Luna
- Co-promoter: Bernolet Sarah
- Fellow: Rognan Hannah
Research team(s)
Funding
- FWO
Project type(s)
- Research Project
Emotions without borders: 91³Ô¹ÏÍøing the verbalization and automatic detection of emotions across languages.
Abstract
Emotions have attracted a lot of attention in psychology, socio- and psycholinguistics and communication science, but since the past decade also in the fields of computational linguistics and natural language processing. In the latter fields, the term emotion detection is used to refer to the task of automatically identifying fine-grained emotions in texts. Research on emotion detection has mainly focused on English, but with the emergence of (multilingual) large language models, the interest in multilingual approaches to emotion detection increased. Meanwhile, state-of-the-art research in psychology have developed new theories about emotion, claiming that emotions are not universal: neither in conceptualization, nor in emotion expression. This might have consequences for how multilingual emotion detection models work. Therefore, it is crucial to investigate differences in emotional language use across languages. Most studies that deal with the cultural component of emotions are limited to studying the translatability of emotion words, or focus on very specific cases and language pairs. In this research project, we will transcend the word level and go beyond the comparison of language pairs by comparing emotion verbalization across ten languages, using methods from computational linguistics. Moreover, we will investigate how state-of-the-art emotion detection models deal with cross-lingual differences in emotion verbalization.Researcher(s)
- Promoter: De Bruyne Luna
- Fellow: Rognan Hannah
Research team(s)
Funding
- BOF
Project type(s)
- Research Project