Jia, Ao; He, Yu; Zhang, Yazhou; Uprety, Sagar; Song, Dawei; Lioma, Christina; (2022) Beyond Emotion: A Multi-Modal Dataset for Human Desire Understanding. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. (pp. pp. 15121-1522). Association for Computational Linguistics: Seattle, WA, USA.

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Abstract

Desire is a strong wish to do or have something, which involves not only a linguistic expression, but also underlying cognitive phenomena driving human feelings. As the most primitive and basic human instinct, conscious desire is often accompanied by a range of emotional responses. As a strikingly understudied task, it is difficult for machines to model and understand desire due to the unavailability of benchmarking datasets with desire and emotion labels. To bridge this gap, we present MSED, the first multi-modal and multi-task sentiment, emotion and desire dataset, which contains 9,190 textimage pairs, with English text. Each multimodal sample is annotated with six desires, three sentiments and six emotions. We also propose the state-of-the-art baselines to evaluate the potential of MSED and show the importance of multi-task and multi-modal clues for desire understanding. We hope this study provides a benchmark for human desire analysis. MSED will be publicly available for research.

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