Towards Social AI: A Survey on Understanding Social Interactions
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910626393096192 |
|---|---|
| author | Lee, Sangmin Li, Minzhi Lai, Bolin Jia, Wenqi Ryan, Fiona Cao, Xu Kara, Ozgur Boote, Bikram Shi, Weiyan Yang, Diyi Rehg, James M. |
| author_facet | Lee, Sangmin Li, Minzhi Lai, Bolin Jia, Wenqi Ryan, Fiona Cao, Xu Kara, Ozgur Boote, Bikram Shi, Weiyan Yang, Diyi Rehg, James M. |
| contents | Social interactions form the foundation of human societies. Artificial intelligence has made significant progress in certain areas, but enabling machines to seamlessly understand social interactions remains an open challenge. It is important to address this gap by endowing machines with social capabilities. We identify three key capabilities needed for effective social understanding: 1) understanding multimodal social cues, 2) understanding multi-party dynamics, and 3) understanding beliefs. Building upon these foundations, we classify and review existing machine learning works on social understanding from the perspectives of verbal, non-verbal, and multimodal social cues. The verbal branch focuses on understanding linguistic signals such as speaker intent, dialogue sentiment, and commonsense reasoning. The non-verbal branch addresses techniques for perceiving social meaning from visual behaviors such as body gestures, gaze patterns, and facial expressions. The multimodal branch covers approaches that integrate verbal and non-verbal multimodal cues to holistically interpret social interactions such as recognizing emotions, conversational dynamics, and social situations. By reviewing the scope and limitations of current approaches and benchmarks, we aim to clarify the development trajectory and illuminate the path towards more comprehensive intelligence for social understanding. We hope this survey will spur further research interest and insights into this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15316 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Social AI: A Survey on Understanding Social Interactions Lee, Sangmin Li, Minzhi Lai, Bolin Jia, Wenqi Ryan, Fiona Cao, Xu Kara, Ozgur Boote, Bikram Shi, Weiyan Yang, Diyi Rehg, James M. Human-Computer Interaction Social interactions form the foundation of human societies. Artificial intelligence has made significant progress in certain areas, but enabling machines to seamlessly understand social interactions remains an open challenge. It is important to address this gap by endowing machines with social capabilities. We identify three key capabilities needed for effective social understanding: 1) understanding multimodal social cues, 2) understanding multi-party dynamics, and 3) understanding beliefs. Building upon these foundations, we classify and review existing machine learning works on social understanding from the perspectives of verbal, non-verbal, and multimodal social cues. The verbal branch focuses on understanding linguistic signals such as speaker intent, dialogue sentiment, and commonsense reasoning. The non-verbal branch addresses techniques for perceiving social meaning from visual behaviors such as body gestures, gaze patterns, and facial expressions. The multimodal branch covers approaches that integrate verbal and non-verbal multimodal cues to holistically interpret social interactions such as recognizing emotions, conversational dynamics, and social situations. By reviewing the scope and limitations of current approaches and benchmarks, we aim to clarify the development trajectory and illuminate the path towards more comprehensive intelligence for social understanding. We hope this survey will spur further research interest and insights into this area. |
| title | Towards Social AI: A Survey on Understanding Social Interactions |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.15316 |