SlowGuess/ACL_2020_OCR huggingface dataset:
Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA, pages 5998-6008.
Daan Verbree, Rutger Rienks, and Dirk Heylen. 2006. Dialogue-act tagging using smart feature selection; results on multiple corpora. In Spoken Language Technology Workshop, 2006. IEEE, pages 70-73. IEEE.
Martin Wöllmer, Felix Weninger, Tobias Knaup, Björn W. Schuller, Congkai Sun, Kenji Sagae, and Louis-Philippe Morency. 2013. Youtube movie reviews: Sentiment analysis in an audio-visual context. IEEE Intelligent Systems, 28(3):46-53.
Yue Yu, Siyao Peng, and Grace Hui Yang. 2019. Modeling long-range context for concurrent dialogue acts recognition. arXiv preprint arXiv:1909.00521.
Amir Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. 2018. Multi-modal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, pages 2236-2246.
Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis-Philippe Morency. 2016. Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages. IEEE Intelligent Systems, 31(6):82-88.
Shiqing Zhang, Shiliang Zhang, Tiejun Huang, Wen Gao, and Qi Tian. 2018. Learning affective features with a hybrid deep model for audio-visual emotion recognition. IEEE Transactions on Circuits and Systems for Video Technology, 28(10):3030-3043.
Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2018. Emotional chatting machine: Emotional conversation generation with internal and external memory. In Thirty-Second AAAI Conference on Artificial Intelligence.
Appendix A. Details of the Original Source Dataset
IEMOCAP: Interactive Emotional Dyadic Motion Capture Database (Busso et al., 2008) is a multi-modal ER dataset. It contains 151 videos of recorded dialogues, with 2 speakers per session for a total of 10 speakers in a two way conversation segmented into utterances amounting to a total of 302 videos across the dataset. Each utterance is annotated for the presence of 10 emotions namely fear, sad, angry, frustrated, excited, surprised, disgust, happy, neutral and others.
MELD: Multi-modal EmotionLines Dataset is also a multi-modal ER dataset derived from the Friends TV series, originally collected by (Poria et al., 2019). It contains 1433 dialogue conversations with multi-party speakers per dialogue
amounting to a total of 13708 utterances across the dataset. Each utterance is annotated for the presence of 7 emotions namely sadness, anger, fear, joy, surprise, disgust, and neutral.