Kavli Affiliate: Yi Zhou
| First 5 Authors: Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas
| Summary:
People worldwide use language in subtle and complex ways to express emotions.
While emotion recognition — an umbrella term for several NLP tasks —
significantly impacts different applications in NLP and other fields, most work
in the area is focused on high-resource languages. Therefore, this has led to
major disparities in research and proposed solutions, especially for
low-resource languages that suffer from the lack of high-quality datasets. In
this paper, we present BRIGHTER– a collection of multilabeled
emotion-annotated datasets in 28 different languages. BRIGHTER covers
predominantly low-resource languages from Africa, Asia, Eastern Europe, and
Latin America, with instances from various domains annotated by fluent
speakers. We describe the data collection and annotation processes and the
challenges of building these datasets. Then, we report different experimental
results for monolingual and crosslingual multi-label emotion identification, as
well as intensity-level emotion recognition. We investigate results with and
without using LLMs and analyse the large variability in performance across
languages and text domains. We show that BRIGHTER datasets are a step towards
bridging the gap in text-based emotion recognition and discuss their impact and
utility.
| Search Query: ArXiv Query: search_query=au:”Yi Zhou”&id_list=&start=0&max_results=3