| Download | - View final version: BRIGHTER: BRIdging the gap in human-annotated textual emotion recognition datasets for 28 languages (PDF, 1.5 MiB)
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| DOI | Resolve DOI: https://doi.org/10.18653/v1/2025.acl-long.436 |
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| Author | Search for: Muhammad, Shamsuddeen Hassan; Search for: Ousidhoum, Nedjma; Search for: Abdulmumin, Idris; Search for: Wahle, Jan Philip; Search for: Ruas, Terry; Search for: Beloucif, Meriem; Search for: De Kock, Christine; Search for: Surange, Nirmal; Search for: Teodorescu, Daniela; Search for: Ahmad, Ibrahim Said; Search for: Adelani, David Ifeoluwa; Search for: Aji, Alham Fikri; Search for: Ali, Felermino D. M. A.; Search for: Alimova, Ilseyar; Search for: Araujo, Vladimir; Search for: Babakov, Nikolay; Search for: Baes, Naomi; Search for: Bucur, Ana-Maria; Search for: Bukula, Andiswa; Search for: Cao, Guanqun; Search for: Tufiño, Rodrigo; Search for: Chevi, Rendi; Search for: Chukwuneke, Chiamaka Ijeoma; Search for: Ciobotaru, Alexandra; Search for: Dementieva, Daryna; Search for: Gadanya, Murja Sani; Search for: Geislinger, Robert; Search for: Gipp, Bela; Search for: Hourrane, Oumaima; Search for: Ignat, Oana; Search for: Lawan, Falalu Ibrahim; Search for: Mabuya, Rooweither; Search for: Mahendra, Rahmad; Search for: Marivate, Vukosi; Search for: Panchenko, Alexander; Search for: Piper, Andrew; Search for: Ferreira, Charles Henrique Porto; Search for: Protasov, Vitaly; Search for: Rutunda, Samuel; Search for: Shrivastava, Manish; Search for: Udrea, Aura Cristina; Search for: Wanzare, Lilian Diana Awuor; Search for: Wu, Sophie; Search for: Wunderlich, Florian Valentin; Search for: Zhafran, Hanif Muhammad; Search for: Zhang, Tianhui; Search for: Zhou, Yi; Search for: Mohammad, Saif M.1ORCID identifier: https://orcid.org/0000-0003-2716-7516 |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | The 63rd Annual Meeting of the Association for Computational Linguistics, July 27 - August 1, 2025, Vienna, Austria |
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| Abstract | People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition–an umbrella term for several NLP tasks–impacts various applications within NLP and beyond, most work in this area has focused on high-resource languages. This has led to significant disparities in research efforts and proposed solutions, particularly for under-resourced languages, which often lack high-quality annotated datasets.In this paper, we present BRIGHTER–a collection of multi-labeled, emotion-annotated datasets in 28 different languages and across several domains. BRIGHTER primarily covers low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. We highlight the challenges related to the data collection and annotation processes, and then report experimental results for monolingual and crosslingual multi-label emotion identification, as well as emotion intensity recognition. We analyse the variability in performance across languages and text domains, both with and without the use of LLMs, and show that the BRIGHTER datasets represent a meaningful step towards addressing the gap in text-based emotion recognition. |
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| Publication date | 2025-07-27 |
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| Publisher | Association for Computational Linguistics |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 9862cc79-bd09-45e4-b778-0950da509925 |
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| Record created | 2025-09-18 |
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| Record modified | 2025-09-19 |
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