The Paradox of Multilingual Emotion Detection (2023)

Abstract: The dominance of English is a well-known issue in NLP research. In this position paper, I turn to state-of-the-art psychological insights to explain why this problem is especially persistent in research on automatic emotion detection, and why the seemingly promising approach of using multilingual models to include lower-resourced languages might not be the desired solution. Instead, I campaign for the use of models that acknowledge linguistic and cultural differences in emotion conceptualization and verbalization. Moreover, I see much potential in NLP to better understand emotions and emotional language use across different languages.

## introduction variation and diversity are inherent to human life, not least to human language. yet, machine learning approaches used in natural language processing (nlp) usually ignore this variation and are biased to a (consciously or subconsciously imposed) norm. @xcite stressed that current nlp applications therefore often amplify societal inequalities and "lead to more adverse outcomes for those that are already marginalized". indeed, we have known for some time that nlp applications show several biases, e.g., racial bias in conversational agents @xcite or gender bias in machine translation @xcite . @xcite therefore campaigns for introducing ethics sheets for ai tasks, in which diversity should be one point to be addressed, including a discussion of the design choices that impact diverse groups of people. besides inequality across social groups, @xcite recently pointed at the inequality across languages as an unwanted bias in nlp (around two thirds of nlp research at top venues would be devoted exclusively to english, which has not changed over the last 10 years). however, instead of merely acknowledging these biases, he argues that it is simple to mitigate inequality amplification, for which he proposes three strategies inspired by policies for reducing carbon emissions: (i) an nlp cap and trade, (ii) an nlp carbon tax, and (iii) nlp car-free sundays. as the language bias is generally towards english, this would in practice mean to (i) distribute quota for publications on english, (ii) impose a cost on researchers submitting papers on english or (iii) a one-year ban on english models. these measures should encourage groups to work on nlp systems and resources for other languages than english. for work on emotion detection (also referred to as automatic emotion recognition or aer) foot_0 as well, we observe a huge bias towards english resources and systems. in order to get some insight in the variety of languages addressed in research on emotion detection and related research fields, i analyzed the papers that were presented at the workshop on computational approaches to subjectivity, sentiment & social media analysis (wassa) since 2011 foot_1 , and counted the number of papers that included other languages than english. foot_2 my findings, shown in figure 1 , are in line with the numbers reported by @xcite : the proportion of papers that not exclusively focused on english fluctuates around one third and did not increase over the years. however, i did observe that there was a remarkable increase in the past two years concerning the total number of languages that were included and the maximum number of languages across papers (see figure 2 ). i believe that this positive trend is fueled by large language models like multilingual bert @xcite , which lend themselves perfectly to (zero or few-shot) transfer learning. indeed, the high number of languages at wassa 2021 and 2022 is largely due to just three papers that include many different languages: lamprinidis et al. (2021 however, even though multilingual models might seem promising for tackling nlp tasks for other (and lower-resourced) languages than enstracts of invited talks and submissions for shared tasks (but, for each shared task, i did include the task description paper). if the paper included other languages than english, it was usually mentioned in the paper title or in the abstract, although sometimes i had to read the dataset description to find out. for a remarkable number of papers, no language was mentioned at all. in almost all of these cases, the papers used english data. plots for the distribution of languages in all wassa discussions are shown in figure 4 in the appendix. glish, the use of multilingual models result in a paradox in the case of emotion detection: using multilingual emotion detection models inherently assumes that different languages deal with emotions the same way. this idea may be in line with classical views on emotion analysis, but goes completely against state-of-the-art evidence in psychology showing that emotions are not universal, but rather culture (and language) dependent @xcite . therefore, i believe the real challenge does not lie in attracting more research on a larger number or greater variety of languages, but in studying emotion detection without falling into universalist ideas and instead acknowledging differences in emotional conceptualization and verbalization across languages. this can be achieved by creating valid datasets (original data written and annotated by native speakers), with label sets that are adjusted to the target language (using native emotion words or emotion representations that go beyond the anglocentric basic emotions). in this position paper, i will discuss state-of-theart psychological findings and their implications for emotion detection in nlp (section 2). i will then expand on these implications by discussing them in the light of current papers in aer research (section 3). next, i will propose some research directions that can be taken in aer to better align with psychological evidence (section 4). finally, my viewpoints are summarized in some concluding thoughts (section 5). ## what psychology teaches us and what it means for aer for a very long time, a universalist view on emotions prevailed. in such a view, it is believed that the way emotions are conceptualized and experienced is the same across different cultures and that emotions are biologically hard-wired. especially the work on facial expressions by ekman and his colleagues, in which participants from different cultures made similar decisions when asked to match emotion words or emotional stories with facial expressions of anger, disgust, fear, joy, sadness and surprise, consolidated the idea of universal emotions @xcite . however, experiments like ekman's are biased by the western perspective of the researcher and influenced by the used research methods (e.g., the choice of emotion words to match with; posed instead of spontaneous emotion expressions). more recent experiments using a greater diversity in research methods and contexts, reveal diversity instead of universality @xcite . moreover, a critical review of literature about the biological basis of emotion categories by @xcite indicates that evidence for the existence of such universal basic emotions is inconclusive. rather, there is "cultural variation in the experience of emotion that is intrinsically driven by cultural differences in emotion categories and concepts" @xcite . in a similar vein, @xcite claim that emotional experience is culturally constructed, which shows itself in cultural differences regarding how people communicate and talk about their emotions. variation in emotion conceptualization and experience on the hand, and concomitantly, emotion expression and verbalization on the other hand, both have consequences for automatic emotion recognition in nlp. ## conclusion in this position paper, i addressed a known issue in nlp, namely the dominance of english. i discussed this issue in the light of automatic emotion detection and argued that this dominance is not limited to the small number of papers that includes other languages than english, but is also reflected in the way current datasets and models are used. it is thus not enough to encourage research on other languages than english, but to address these languages the right way, without assuming that emotions are conceptualized and verbalized in a universal way. therefore, it is crucial to create valid data, i.e., original data from the target language (not translated) and annotated by native speakers. label sets should be adjusted to the target language, using native emotion words and preferably combined with labels for core affect. moreover, i see much potential in nlp to better understand how emotional language use differs across languages. that information can subsequently help to reveal how current multilingual models deal with such differences, or even to make them more language inclusive.