TY - GEN
T1 - Emotion Recognition in Conversation from Variable-Length Context
AU - Zhang, Mian
AU - Zhou, Xiabing
AU - Chen, Wenliang
AU - Zhang, Min
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Existing approaches to Emotion Recognition in Conversation (ERC) use a fixed context window to recognize speakers' emotion, which may lead to either scantiness of key context or interference of redundant context. In response, we explore the benefits of variable-length context and propose a more effective approach to ERC. In our approach, we leverage different context windows when predicting the emotion of different utterances. New modules are included to realize variable-length context: 1) two speaker-aware units, which explicitly model inner- and inter-speaker dependencies to form distilled conversational context and 2) a top-k normalization layer, which determines the most proper context windows from the conversational context to predict emotion. Experiments and ablation study show that our approach outperforms several strong baselines on three public datasets.
AB - Existing approaches to Emotion Recognition in Conversation (ERC) use a fixed context window to recognize speakers' emotion, which may lead to either scantiness of key context or interference of redundant context. In response, we explore the benefits of variable-length context and propose a more effective approach to ERC. In our approach, we leverage different context windows when predicting the emotion of different utterances. New modules are included to realize variable-length context: 1) two speaker-aware units, which explicitly model inner- and inter-speaker dependencies to form distilled conversational context and 2) a top-k normalization layer, which determines the most proper context windows from the conversational context to predict emotion. Experiments and ablation study show that our approach outperforms several strong baselines on three public datasets.
KW - conversation modeling
KW - emotion recognition
KW - variable context
UR - https://www.scopus.com/pages/publications/85177546966
U2 - 10.1109/ICASSP49357.2023.10096161
DO - 10.1109/ICASSP49357.2023.10096161
M3 - 会议稿件
AN - SCOPUS:85177546966
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -