mlsanityData to 7 Oct 2026

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

arXiv 1810.02508 ↗First posted 5 Oct 2018cs.CLcited by 636

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Cited by 30 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http:// affective-meld.github.io.

Key facts. Cited by 636 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2018.

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Citing papers per year: 2021: 45; 2022: 53; 2023: 92; 2024: 134; 2025: 145; 2026: 167.

636 papers.

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  1. 2023Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Modelscited by 591
  2. 2024Qwen2-Audio Technical Reportcited by 543
  3. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 294
  4. 2024MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmarkcited by 281
  5. 2021Beyond Goldfish Memory: Long-Term Open-Domain Conversationcited by 266
  6. 2024AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehensioncited by 215
  7. 2025Kimi-Audio Technical Reportcited by 210
  8. 2023emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representationcited by 196
  9. 2024Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilitiescited by 183
  10. 2021MultiBench: Multiscale Benchmarks for Multimodal Representation Learningcited by 143
  11. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  12. 2025Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilitiescited by 127
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  1. 2024AudioBench: A Universal Benchmark for Audio Large Language Modelscited by 124
  2. 2023Multimodal Prompting with Missing Modalities for Visual Recognitioncited by 121
  3. 2024FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMscited by 118
  4. 2025Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokenscited by 109
  5. 2024Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Expertscited by 107
  6. 2025Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalitiescited by 101
  7. 2025MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmarkcited by 101
  8. 2021MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversationcited by 99
  9. 2023Emotional Speech-Driven Animation with Content-Emotion Disentanglementcited by 98
  10. 2021Directed Acyclic Graph Network for Conversational Emotion Recognitioncited by 97
  11. 2025Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Modelscited by 94
  12. 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 89
  13. 2024WavChat: A Survey of Spoken Dialogue Modelscited by 88
  14. 2023LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPTcited by 77
  15. 2021FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoderscited by 75
  16. 2023PLACES: Prompting Language Models for Social Conversation Synthesiscited by 72
  17. 2021DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversationscited by 71
  18. 2022DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speechcited by 67
  19. 2024SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Wordscited by 67
  20. 2026MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interactioncited by 64
  21. 2022UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognitioncited by 61
  22. 2021EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTacited by 59
  23. 2021Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detectioncited by 56
  24. 2023Is ChatGPT Equipped with Emotional Dialogue Capabilities?cited by 54
  25. 2023Towards Interpretable Mental Health Analysis with Large Language Modelscited by 53
  26. 2024EmoBench: Evaluating the Emotional Intelligence of Large Language Modelscited by 53
  27. 2025OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLMcited by 53
  28. 2022MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversationscited by 52
  29. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  30. 2022M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Databasecited by 50
  31. 2022EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognitioncited by 48
  32. 2022M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversationcited by 47
  33. 2023Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversationscited by 47
  34. 2025DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignmentcited by 47
  35. 2022Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Predictioncited by 45
  36. 2024Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversationscited by 45
  37. 2022Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Modulecited by 43
  38. 2025AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Modelscited by 43

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