CMMA: Benchmarking Multi-Affection Detection in Chinese Multi-Modal Conversations
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CMMA : Benchmarking Multi-Affection Detection in Chinese Multi-Modal Conversations. / Zhang, Yazhou; Yu, Yang; Guo, Qing ; Wang, Benyou ; Zhao, Dongming ; Uprety, Sagar ; Song, Dawei; Li, Qiuchi; Qin, Jing .
Advances in Neural Information Processing Systems 36 pre-proceedings (NeurIPS 2023). NeurIPS Proceedings, 2023. (Advances in Neural Information Processing Systems, Bind 36).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - CMMA
T2 - 37th Conference on Neural Information Processing Systems - NeurIPS 2023
AU - Zhang, Yazhou
AU - Yu, Yang
AU - Guo, Qing
AU - Wang, Benyou
AU - Zhao, Dongming
AU - Uprety, Sagar
AU - Song, Dawei
AU - Li, Qiuchi
AU - Qin, Jing
PY - 2023
Y1 - 2023
N2 - Human communication has a multi-modal and multi-affection nature. The inter-relatedness of different emotions and sentiments poses a challenge to jointly detect multiple human affections with multi-modal clues. Recent advances in this field employed multi-task learning paradigms to render the inter-relatedness across tasks, but the scarcity of publicly available resources sets a limit to the potential of works. To fill this gap, we build the first Chinese Multi-modal Multi-Affection conversation (CMMA) dataset, which contains 3,000 multi-party conversations and 21,795 multi-modal utterances collected from various styles of TV-series. CMMA contains a wide variety of affection labels, including sentiment, emotion, sarcasm and humor, as well as the novel inter-correlations values between certain pairs of tasks. Moreover, it provides the topic and speaker information in conversations, which promotes better modeling of conversational context. On the dataset, we empirically analyze the influence of different data modalities and conversational contexts on different affection analysis tasks, and exhibit the practical benefit of inter-task correlations. The full dataset will be publicly available for research\footnote{https://github.com/annoymity2022/Chinese-Dataset}
AB - Human communication has a multi-modal and multi-affection nature. The inter-relatedness of different emotions and sentiments poses a challenge to jointly detect multiple human affections with multi-modal clues. Recent advances in this field employed multi-task learning paradigms to render the inter-relatedness across tasks, but the scarcity of publicly available resources sets a limit to the potential of works. To fill this gap, we build the first Chinese Multi-modal Multi-Affection conversation (CMMA) dataset, which contains 3,000 multi-party conversations and 21,795 multi-modal utterances collected from various styles of TV-series. CMMA contains a wide variety of affection labels, including sentiment, emotion, sarcasm and humor, as well as the novel inter-correlations values between certain pairs of tasks. Moreover, it provides the topic and speaker information in conversations, which promotes better modeling of conversational context. On the dataset, we empirically analyze the influence of different data modalities and conversational contexts on different affection analysis tasks, and exhibit the practical benefit of inter-task correlations. The full dataset will be publicly available for research\footnote{https://github.com/annoymity2022/Chinese-Dataset}
M3 - Article in proceedings
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 36 pre-proceedings (NeurIPS 2023)
PB - NeurIPS Proceedings
Y2 - 10 December 2023 through 16 December 2023
ER -
ID: 383796510