Emotion Detection in Unfix-length-Context Conversation
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Abstract (arXiv)
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 studies show that our approach outperforms several strong baselines on three public datasets.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 13 Feb 2023.
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References · 15
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 649
- 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 636
- 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
- 2018DialogueRNN: An Attentive RNN for Emotion Detection in Conversationscited by 216
- 2019DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversationcited by 184
- 2020COSMIC: COmmonSense knowledge for eMotion Identification in Conversationscited by 135
- 2019Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advancescited by 118
- 2017Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networkscited by 102
- 2019Knowledge-Enriched Transformer for Emotion Detection in Textual Conversationscited by 92
- 2020Utterance-level Dialogue Understanding: An Empirical Studycited by 10
- 2022HieNet: Bidirectional Hierarchy Framework for Automated ICD Codingcited by 3
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