@inproceedings{sasu-etal-2025-akan,
title = "{A}kan Cinematic Emotions ({ACE}): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues",
author = "Sasu, David and
Wu, Zehui and
Gong, Ziwei and
Chen, Run and
Shi, Pengyuan and
Ai, Lin and
Hirschberg, Julia and
Schluter, Natalie",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.510/",
doi = "10.18653/v1/2025.findings-acl.510",
pages = "9820--9831",
ISBN = "979-8-89176-256-5",
abstract = "In this paper, we introduce the Akan Cinematic Emotions (AkaCE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of resources for low-resource languages in emotion recognition research. AkaCE, developed for the Akan language, contains 385 emotion-labeled dialogues and 6162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations. The presence of prosodic labels in this dataset also makes it the first prosodically annotated African language dataset. We demonstrate the quality and utility of AkaCE through experiments using state-of-the-art emotion recognition methods, establishing solid baselines for future research. We hope AkaCE inspires further work on inclusive, linguistically and culturally diverse NLP resources."
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<abstract>In this paper, we introduce the Akan Cinematic Emotions (AkaCE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of resources for low-resource languages in emotion recognition research. AkaCE, developed for the Akan language, contains 385 emotion-labeled dialogues and 6162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations. The presence of prosodic labels in this dataset also makes it the first prosodically annotated African language dataset. We demonstrate the quality and utility of AkaCE through experiments using state-of-the-art emotion recognition methods, establishing solid baselines for future research. We hope AkaCE inspires further work on inclusive, linguistically and culturally diverse NLP resources.</abstract>
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%0 Conference Proceedings
%T Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues
%A Sasu, David
%A Wu, Zehui
%A Gong, Ziwei
%A Chen, Run
%A Shi, Pengyuan
%A Ai, Lin
%A Hirschberg, Julia
%A Schluter, Natalie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F sasu-etal-2025-akan
%X In this paper, we introduce the Akan Cinematic Emotions (AkaCE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of resources for low-resource languages in emotion recognition research. AkaCE, developed for the Akan language, contains 385 emotion-labeled dialogues and 6162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations. The presence of prosodic labels in this dataset also makes it the first prosodically annotated African language dataset. We demonstrate the quality and utility of AkaCE through experiments using state-of-the-art emotion recognition methods, establishing solid baselines for future research. We hope AkaCE inspires further work on inclusive, linguistically and culturally diverse NLP resources.
%R 10.18653/v1/2025.findings-acl.510
%U https://aclanthology.org/2025.findings-acl.510/
%U https://doi.org/10.18653/v1/2025.findings-acl.510
%P 9820-9831
Markdown (Informal)
[Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues](https://aclanthology.org/2025.findings-acl.510/) (Sasu et al., Findings 2025)
ACL
- David Sasu, Zehui Wu, Ziwei Gong, Run Chen, Pengyuan Shi, Lin Ai, Julia Hirschberg, and Natalie Schluter. 2025. Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues. In Findings of the Association for Computational Linguistics: ACL 2025, pages 9820–9831, Vienna, Austria. Association for Computational Linguistics.