@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://preview.aclanthology.org/mtsummit-25-ingestion/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."
}