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The field of emotion recognition of conversation (ERC) has been focusing on separating sentence feature encoding and context modeling, lacking exploration in generative paradigms based on unified designs.
Roberta: A robustly optimized bert pretraining approach
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Multi-task learning with auxiliary speaker identification for conversational emotion recognition
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Semeval-2016 task 5: Aspect based sentiment analysis
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Context-dependent sentiment analysis in user-generated videos
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Emotion detection on tv show transcripts with sequence-based convolutional neural networks
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Icon: Interactive conversational memory network for multimodal emotion detection
Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, and Roger Zimmermann. 2018 · 2018
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Meld: A multimodal multi-party dataset for emotion recognition in conversations
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. 2018 · 2018
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Dialoguegcn: A graph convolutional neural network for emotion recognition in conversation
Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, and Alexander Gelbukh. 2019 · 2019
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Dialoguernn: An attentive rnn for emotion detection in conversations
Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, and Erik Cambria. 2019 · 2019
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Language models are few-shot learners
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Relation-aware graph attention networks with relational position encodings for emotion recognition in conversations
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Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Past, present, and future: Conversational emotion recognition through structural modeling of psychological knowledge
Jiangnan Li, Zheng Lin, Peng Fu, and Weiping Wang. 2021 · 2021
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M6: Multi-modality-to-multi-modality multitask mega-transformer for unified pretraining
Junyang Lin, Rui Men, An Yang, Chang Zhou, Yichang Zhang, Peng Wang, Jingren Zhou, Jie Tang, and Hongxia Yang. 2021 · 2021
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Directed acyclic graph network for conversational emotion recognition
Weizhou Shen, Siyue Wu, Yunyi Yang, and Xiaojun Quan. 2021 · 2021
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Shanglin Lei, Xiaoping Wang, Guanting Dong, Jiang Li, and Yingjian Liu. 2023 · 2023
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OpenAI. 2023 · 2023
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Making language models better tool learners with execution feedback
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Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface
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Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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M2fnet: Multi-modal fusion network for emotion recognition in conversation
Vishal Chudasama, Purbayan Kar, Ashish Gudmalwar, Nirmesh Shah, Pankaj Wasnik, and Naoyuki Onoe. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Unified structure generation for universal information extraction
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Supervised prototypical contrastive learning for emotion recognition in conversation
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Chain-of-thought prompting elicits reasoning in large language models
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A survey of large language models
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We-math: Does your large multimodal model achieve human-like mathematical reasoning?
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