arXiv:2108.12009
공식 코드 · Papers with Code
EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa
원문 초록
We present EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa, a simple yet expressive scheme of solving the ERC (emotion recognition in conversation) task. By simply prepending speaker names to utterances and inserting separation tokens between the utterances in a dialogue, EmoBERTa can learn intra- and inter- speaker states and context to predict the emotion of a current speaker, in an end-to-end manner. Our experiments show that we reach a new state of the art on the two popular ERC datasets using a basic and straight-forward approach. We've open sourced our code and models at https://github.com/tae898/erc.
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Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations
AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations
TED: Turn Emphasis with Dialogue Feature Attention for Emotion Recognition in Conversation
토론
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