Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues
This addresses the problem of limited NLP resources for African languages, enabling more inclusive and culturally diverse emotion recognition research, though it is incremental as it applies existing methods to new data.
The paper tackles the lack of emotion recognition resources for low-resource languages by introducing the ACE dataset, the first multimodal emotion dialogue dataset for an African language, containing 385 dialogues and 6,162 utterances across audio, visual, and textual modalities, and establishing baselines with state-of-the-art methods.
In this paper, we introduce the Akan Conversation Emotion (ACE) 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. ACE, developed for the Akan language, contains 385 emotion-labeled dialogues and 6,162 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 ACE through experiments using state-of-the-art emotion recognition methods, establishing solid baselines for future research. We hope ACE inspires further work on inclusive, linguistically and culturally diverse NLP resources.