{"url":"/sota/emotion-recognition-in-conversation-on","task":{"name":"Emotion Recognition in Conversation","url":"/task/emotion-recognition-in-conversation","note":null},"dataset":{"name":"IEMOCAP","url":"/dataset/iemocap"},"category":"Computer Vision","categories":["Audio","Computer Vision","Miscellaneous","Natural Language Processing","Speech"],"category_note":null,"description":"Given the transcript of a conversation along with speaker information of each constituent utterance, the ERC task aims to identify the emotion of each utterance from several pre-defined emotions. Formally, given the input sequence of N number of utterances [(u1, p1), (u2, p2), . . . , (uN , pN )], where each utterance ui = [ui,1, ui,2, . . . , ui,T ] consists of T words ui,j and spoken by party pi, the task is to predict the emotion label ei of each utterance ui.\r\n.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Weighted-F1","Accuracy","Micro-F1","Macro-F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Weighted-F1":"higher","Accuracy":"higher","Micro-F1":"higher","Macro-F1":"higher"}},"counts":{"rows":59,"rows_with_code":41,"rows_with_paper_page":59,"rows_dated":54,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SDT","metrics":{"Accuracy":"73.95","Weighted-F1":"74.08"},"uses_additional_data":false,"paper_date":"2023-10-31","paper":"/paper/a-transformer-based-model-with-self","paper_url":"https://arxiv.org/abs/2310.20494v1","paper_title":"A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations","code":"https://github.com/butterfliesss/sdt","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"GraphSmile","metrics":{"Accuracy":"72.77","Weighted-F1":"72.81"},"uses_additional_data":false,"paper_date":"2024-07-31","paper":"/paper/2407-21536","paper_url":"https://arxiv.org/abs/2407.21536v1","paper_title":"Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition","code":"https://github.com/lijfrank-open/GraphSmile","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"SpeechCueLLM","metrics":{"Weighted-F1":"72.596"},"uses_additional_data":false,"paper_date":"2024-07-31","paper":"/paper/2407-21315","paper_url":"https://arxiv.org/abs/2407.21315v4","paper_title":"Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances","code":"https://github.com/zehuiwu/SpeechCueLLM","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"MPT-HCL","metrics":{"Accuracy":"72.83","Weighted-F1":"72.51"},"uses_additional_data":false,"paper_date":"2023-10-04","paper":"/paper/multimodal-prompt-transformer-with-hybrid","paper_url":"https://arxiv.org/abs/2310.04456v1","paper_title":"Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"CKERC","metrics":{"Weighted-F1":"72.40"},"uses_additional_data":false,"paper_date":"2024-03-12","paper":"/paper/ckerc-joint-large-language-models-with","paper_url":"https://arxiv.org/abs/2403.07260v1","paper_title":"CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"EmoCaps","metrics":{"Weighted-F1":"71.77"},"uses_additional_data":false,"paper_date":"2022-03-25","paper":"/paper/emocaps-emotion-capsule-based-model-for","paper_url":"https://arxiv.org/abs/2203.13504v1","paper_title":"EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":7,"model":"DF-ERC","metrics":{"Accuracy":"71.84","Weighted-F1":"71.75"},"uses_additional_data":false,"paper_date":"2023-08-08","paper":"/paper/revisiting-disentanglement-and-fusion-on","paper_url":"https://arxiv.org/abs/2308.04502v2","paper_title":"Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":8,"model":"InstructERC","metrics":{"Accuracy":"71.68","Weighted-F1":"71.39"},"uses_additional_data":false,"paper_date":"2023-09-21","paper":"/paper/instructerc-reforming-emotion-recognition-in","paper_url":"https://arxiv.org/abs/2309.11911v6","paper_title":"InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models","code":"https://github.com/LIN-SHANG/InstructERC","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":9,"model":"BiosERC","metrics":{"Weighted-F1":"71.19"},"uses_additional_data":false,"paper_date":"2024-07-05","paper":"/paper/bioserc-integrating-biography-speakers","paper_url":"https://arxiv.org/abs/2407.04279v1","paper_title":"BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks","code":"https://github.com/yingjie7/BiosERC","n_code_links":1,"syntology":null},{"rank_in_archive_order":10,"model":"SAMGN","metrics":{"Weighted-F1":"71.11"},"uses_additional_data":false,"paper_date":"2022-02-07","paper":"/paper/structure-aware-transformer-for-graph","paper_url":"https://arxiv.org/abs/2202.03036v3","paper_title":"Structure-Aware Transformer for Graph Representation Learning","code":"https://github.com/BorgwardtLab/SAT","n_code_links":3,"syntology":{"n_ran":4,"n_unverified":5,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":11,"model":"CFN-ESA","metrics":{"Accuracy":"70.78","Weighted-F1":"71.04"},"uses_additional_data":false,"paper_date":"2023-07-28","paper":"/paper/cfn-esa-a-cross-modal-fusion-network-with","paper_url":"https://arxiv.org/abs/2307.15432v2","paper_title":"CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition","code":"https://github.com/lijfrank-open/CFN-ESA","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"ELR-GNN","metrics":{"Accuracy":"70.6","Weighted-F1":"70.9"},"uses_additional_data":false,"paper_date":"2024-06-27","paper":"/paper/efficient-long-distance-latent-relation-aware","paper_url":"https://arxiv.org/abs/2407.00119v2","paper_title":"Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":13,"model":"UniMSE","metrics":{"Accuracy":"70.56","Weighted-F1":"70.66"},"uses_additional_data":false,"paper_date":"2022-11-21","paper":"/paper/unimse-towards-unified-multimodal-sentiment","paper_url":"https://arxiv.org/abs/2211.11256v1","paper_title":"UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition","code":"https://github.com/lemei/unimse","n_code_links":1,"syntology":null},{"rank_in_archive_order":14,"model":"TelME","metrics":{"Weighted-F1":"70.48"},"uses_additional_data":false,"paper_date":"2024-01-16","paper":"/paper/telme-teacher-leading-multimodal-fusion","paper_url":"https://arxiv.org/abs/2401.12987v2","paper_title":"TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation","code":"https://github.com/yuntaeyang/telme","n_code_links":1,"syntology":null},{"rank_in_archive_order":15,"model":"EACL","metrics":{"Weighted-F1":"70.41"},"uses_additional_data":false,"paper_date":"2024-03-29","paper":"/paper/emotion-anchored-contrastive-learning","paper_url":"https://arxiv.org/abs/2403.20289v1","paper_title":"Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation","code":"https://github.com/yu-fangxu/eacl","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"LSDGNN+ICL","metrics":{"Weighted-F1":"70.24"},"uses_additional_data":false,"paper_date":"2025-07-21","paper":"/paper/long-short-distance-graph-neural-networks-and","paper_url":"https://arxiv.org/abs/2507.15205v1","paper_title":"Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation","code":"https://github.com/LiXinran6/LSDGNN_ICL","n_code_links":1,"syntology":null},{"rank_in_archive_order":17,"model":"GA2MIF","metrics":{"Accuracy":"69.75","Weighted-F1":"70.00"},"uses_additional_data":false,"paper_date":"2022-07-25","paper":"/paper/ga2mif-graph-and-attention-based-two-stage","paper_url":"https://arxiv.org/abs/2207.11900v6","paper_title":"GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection","code":"https://github.com/lijfrank-open/GA2MIF","n_code_links":1,"syntology":null},{"rank_in_archive_order":18,"model":"M2FNet","metrics":{"Accuracy":"69.69","Weighted-F1":"69.86"},"uses_additional_data":false,"paper_date":"2022-06-05","paper":"/paper/m2fnet-multi-modal-fusion-network-for-emotion","paper_url":"https://arxiv.org/abs/2206.02187v1","paper_title":"M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"SPCL-CL-ERC","metrics":{"Weighted-F1":"69.74"},"uses_additional_data":false,"paper_date":"2022-10-17","paper":"/paper/supervised-prototypical-contrastive-learning","paper_url":"https://arxiv.org/abs/2210.08713v2","paper_title":"Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation","code":"https://github.com/caskcsg/spcl","n_code_links":1,"syntology":null},{"rank_in_archive_order":20,"model":"SACL-LSTM (one seed)","metrics":{"Accuracy":"69.62","Weighted-F1":"69.70"},"uses_additional_data":false,"paper_date":"2023-06-02","paper":"/paper/supervised-adversarial-contrastive-learning","paper_url":"https://arxiv.org/abs/2306.01505v2","paper_title":"Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations","code":"https://github.com/zerohd4869/sacl","n_code_links":1,"syntology":null},{"rank_in_archive_order":21,"model":"EmotionIC","metrics":{"Accuracy":"69.44","Weighted-F1":"69.61"},"uses_additional_data":false,"paper_date":"2023-03-20","paper":"/paper/emotionic-emotional-inertia-and-contagion","paper_url":"https://arxiv.org/abs/2303.11117v5","paper_title":"EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation","code":"https://github.com/lijfrank-open/EmotionIC","n_code_links":1,"syntology":null},{"rank_in_archive_order":22,"model":"FATRER","metrics":{"Accuracy":"69.69","Weighted-F1":"69.35"},"uses_additional_data":false,"paper_date":"2023-07-23","paper":"/paper/fatrer-full-attention-topic-regularizer-for","paper_url":"https://arxiv.org/abs/2307.12221v1","paper_title":"FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition","code":"https://github.com/ludybupt/FATRER","n_code_links":1,"syntology":null},{"rank_in_archive_order":23,"model":"SACL-LSTM","metrics":{"Accuracy":"69.08","Weighted-F1":"69.22"},"uses_additional_data":false,"paper_date":"2023-06-02","paper":"/paper/supervised-adversarial-contrastive-learning","paper_url":"https://arxiv.org/abs/2306.01505v2","paper_title":"Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations","code":"https://github.com/zerohd4869/sacl","n_code_links":1,"syntology":null},{"rank_in_archive_order":24,"model":"GraphCFC","metrics":{"Accuracy":"69.13","Weighted-F1":"68.91"},"uses_additional_data":false,"paper_date":"2022-07-06","paper":"/paper/graphcfc-a-directed-graph-based-cross-modal","paper_url":"https://arxiv.org/abs/2207.12261v4","paper_title":"GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition","code":"https://github.com/lijfrank-open/GraphCFC","n_code_links":1,"syntology":null},{"rank_in_archive_order":25,"model":"DAG-ERC+HCL","metrics":{"Weighted-F1":"68.73"},"uses_additional_data":false,"paper_date":"2021-12-22","paper":"/paper/hybrid-curriculum-learning-for-emotion","paper_url":"https://arxiv.org/abs/2112.11718v2","paper_title":"Hybrid Curriculum Learning for Emotion Recognition in Conversation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":26,"model":"S+PAGE","metrics":{"Weighted-F1":"68.72"},"uses_additional_data":false,"paper_date":"2021-12-23","paper":"/paper/s-page-a-speaker-and-position-aware-graph","paper_url":"https://arxiv.org/abs/2112.12389v1","paper_title":"S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":27,"model":"EmoBERTa","metrics":{"Weighted-F1":"68.57"},"uses_additional_data":false,"paper_date":"2021-08-26","paper":"/paper/emoberta-speaker-aware-emotion-recognition-in","paper_url":"https://arxiv.org/abs/2108.12009v1","paper_title":"EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa","code":"https://github.com/tae898/erc","n_code_links":1,"syntology":null},{"rank_in_archive_order":28,"model":"MM-DFN","metrics":{"Accuracy":"68.21","Weighted-F1":"68.18"},"uses_additional_data":false,"paper_date":"2022-03-04","paper":"/paper/mm-dfn-multimodal-dynamic-fusion-network-for","paper_url":"https://arxiv.org/abs/2203.02385v1","paper_title":"MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations","code":"https://github.com/zerohd4869/mm-dfn","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":29,"model":"DAG-ERC","metrics":{"Weighted-F1":"68.03"},"uses_additional_data":false,"paper_date":"2021-05-27","paper":"/paper/directed-acyclic-graph-network-for","paper_url":"https://arxiv.org/abs/2105.12907v2","paper_title":"Directed Acyclic Graph Network for Conversational Emotion Recognition","code":"https://github.com/shenwzh3/DAG-ERC","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":30,"model":"AccumWR","metrics":{"Weighted-F1":"67.65"},"uses_additional_data":false,"paper_date":"2023-11-06","paper":"/paper/accumulating-word-representations-in-multi","paper_url":"https://ieeexplore.ieee.org/document/10299463","paper_title":"Accumulating Word Representations in Multi-level Context Integration for ERC Task","code":"https://github.com/yingjie7/per_erc","n_code_links":1,"syntology":null},{"rank_in_archive_order":31,"model":"DialogueCRN+RoBERTa","metrics":{"Accuracy":"67.39","Weighted-F1":"67.53"},"uses_additional_data":false,"paper_date":"2021-06-03","paper":"/paper/dialoguecrn-contextual-reasoning-networks-for","paper_url":"https://arxiv.org/abs/2106.01978v2","paper_title":"DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations","code":"https://github.com/zerohd4869/mm-dfn","n_code_links":2,"syntology":null},{"rank_in_archive_order":32,"model":"CESTa","metrics":{"Weighted-F1":"67.1"},"uses_additional_data":false,"paper_date":"2020-07-01","paper":"/paper/contextualized-emotion-recognition-in","paper_url":"https://aclanthology.org/2020.sigdial-1.23","paper_title":"Contextualized Emotion Recognition in Conversation as Sequence Tagging","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":33,"model":"KI-Net","metrics":{"Micro-F1":"64.10","Weighted-F1":"67.00"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/knowledge-interactive-network-with-sentiment","paper_url":"https://aclanthology.org/2021.findings-emnlp.245","paper_title":"Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":34,"model":"SKAIG-ERC","metrics":{"Weighted-F1":"66.98"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/past-present-and-future-conversational","paper_url":"https://aclanthology.org/2021.findings-emnlp.104","paper_title":"Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge","code":"https://github.com/leqsnan/skaig-erc","n_code_links":1,"syntology":null},{"rank_in_archive_order":35,"model":"SumAggGIN","metrics":{"Weighted-F1":"66.96"},"uses_additional_data":false,"paper_date":"2020-12-01","paper":"/paper/summarize-before-aggregate-a-global-to-local","paper_url":"https://aclanthology.org/2020.coling-main.367","paper_title":"Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":36,"model":"CoMPM","metrics":{"Accuracy":"66.76","Weighted-F1":"66.61"},"uses_additional_data":false,"paper_date":"2021-08-26","paper":"/paper/compm-context-modeling-with-speaker-s-pre","paper_url":"https://arxiv.org/abs/2108.11626v3","paper_title":"CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation","code":"https://github.com/rungjoo/compm","n_code_links":1,"syntology":null},{"rank_in_archive_order":37,"model":"DialogueCRN","metrics":{"Accuracy":"66.05","Weighted-F1":"66.33"},"uses_additional_data":false,"paper_date":"2021-06-03","paper":"/paper/dialoguecrn-contextual-reasoning-networks-for","paper_url":"https://arxiv.org/abs/2106.01978v2","paper_title":"DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations","code":"https://github.com/zerohd4869/mm-dfn","n_code_links":2,"syntology":null},{"rank_in_archive_order":38,"model":"DialogXL","metrics":{"Accuracy":"66.3","Weighted-F1":"66.2"},"uses_additional_data":false,"paper_date":"2020-12-16","paper":"/paper/dialogxl-all-in-one-xlnet-for-multi-party","paper_url":"https://arxiv.org/abs/2012.08695v1","paper_title":"DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition","code":"https://github.com/shenwzh3/DialogXL","n_code_links":4,"syntology":null},{"rank_in_archive_order":39,"model":"M2FNet-Text","metrics":{"Accuracy":"66.05","Macro-F1":"66.38","Weighted-F1":"66.2"},"uses_additional_data":false,"paper_date":"2022-06-05","paper":"/paper/m2fnet-multi-modal-fusion-network-for-emotion","paper_url":"https://arxiv.org/abs/2206.02187v1","paper_title":"M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":40,"model":"TRMSM-Att","metrics":{"Weighted-F1":"65.94"},"uses_additional_data":false,"paper_date":"2020-12-29","paper":"/paper/a-hierarchical-transformer-with-speaker","paper_url":"https://arxiv.org/abs/2012.14781v1","paper_title":"A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation","code":"https://github.com/leqsnan/skaig-erc","n_code_links":1,"syntology":null},{"rank_in_archive_order":41,"model":"COIN (w/o pretraining)","metrics":{"Weighted-F1":"65.74"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/coin-conversational-interactive-networks-for","paper_url":"https://aclanthology.org/2021.maiworkshop-1.3","paper_title":"COIN: Conversational Interactive Networks for Emotion Recognition in Conversation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":42,"model":"Pretrained Hierarchical Transformer","metrics":{"Accuracy":"66.05","Weighted-F1":"65.37"},"uses_additional_data":false,"paper_date":"2020-09-23","paper":"/paper/hierarchical-pre-training-for-sequence","paper_url":"https://arxiv.org/abs/2009.11152v3","paper_title":"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":43,"model":"COSMIC","metrics":{"Weighted-F1":"65.30"},"uses_additional_data":false,"paper_date":"2020-10-06","paper":"/paper/cosmic-commonsense-knowledge-for-emotion","paper_url":"https://arxiv.org/abs/2010.02795v1","paper_title":"COSMIC: COmmonSense knowledge for eMotion Identification in Conversations","code":"https://github.com/declare-lab/conv-emotion","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":2,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":44,"model":"RGAT-ERC","metrics":{"Weighted-F1":"65.28"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/relation-aware-graph-attention-networks-with","paper_url":"https://aclanthology.org/2020.emnlp-main.597","paper_title":"Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations","code":"https://github.com/KomorebiLHX/Emotion-Recognition-in-Conversations","n_code_links":1,"syntology":null},{"rank_in_archive_order":45,"model":"BiERU-lc","metrics":{"Weighted-F1":"65.22"},"uses_additional_data":false,"paper_date":"2020-05-31","paper":"/paper/bieru-bidirectional-emotional-recurrent-unit","paper_url":"https://arxiv.org/abs/2006.00492v3","paper_title":"BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis","code":"https://github.com/Maxwe11y/BiERU","n_code_links":1,"syntology":null},{"rank_in_archive_order":46,"model":"HiTrans","metrics":{"Accuracy":"66.11","Weighted-F1":"64.65"},"uses_additional_data":false,"paper_date":"2020-12-01","paper":"/paper/hitrans-a-transformer-based-context-and","paper_url":"https://aclanthology.org/2020.coling-main.370","paper_title":"HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":47,"model":"Iterative","metrics":{"Weighted-F1":"64.5"},"uses_additional_data":false,"paper_date":"2020-12-01","paper":"/paper/an-iterative-emotion-interaction-network-for","paper_url":"https://aclanthology.org/2020.coling-main.360","paper_title":"An Iterative Emotion Interaction Network for Emotion Recognition in Conversations","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":48,"model":"DialogueGCN","metrics":{"Weighted-F1":"64.37"},"uses_additional_data":false,"paper_date":"2019-08-30","paper":"/paper/dialoguegcn-a-graph-convolutional-neural","paper_url":"https://arxiv.org/abs/1908.11540v1","paper_title":"DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation","code":"https://github.com/SenticNet/conv-emotion","n_code_links":2,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":49,"model":"ERMC-DisGCN","metrics":{"Accuracy":"65.25","Macro-F1":"63.43","Weighted-F1":"64.18"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/a-discourse-aware-graph-neural-network-for","paper_url":"https://aclanthology.org/2021.findings-emnlp.252","paper_title":"A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":50,"model":"BiF-AGRU","metrics":{"Weighted-F1":"64.10"},"uses_additional_data":false,"paper_date":"2019-11-20","paper":"/paper/real-time-emotion-recognition-via-attention","paper_url":"https://arxiv.org/abs/1911.09075v1","paper_title":"Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network","code":"https://github.com/wxjiao/AGHMN","n_code_links":1,"syntology":null},{"rank_in_archive_order":51,"model":"DialogueRNN","metrics":{"Accuracy":"63.5","Weighted-F1":"63.5"},"uses_additional_data":false,"paper_date":"2018-11-01","paper":"/paper/dialoguernn-an-attentive-rnn-for-emotion","paper_url":"https://arxiv.org/abs/1811.00405v4","paper_title":"DialogueRNN: An Attentive RNN for Emotion Detection in Conversations","code":"https://github.com/SenticNet/conv-emotion","n_code_links":2,"syntology":null},{"rank_in_archive_order":52,"model":"Attention-BLSTM","metrics":{"Accuracy":"65.9","Weighted-F1":"62.9"},"uses_additional_data":false,"paper_date":"2018-11-09","paper":"/paper/integrating-recurrence-dynamics-for-speech","paper_url":"http://arxiv.org/abs/1811.04133v1","paper_title":"Integrating Recurrence Dynamics for Speech Emotion Recognition","code":"https://github.com/etzinis/nldrp","n_code_links":1,"syntology":null},{"rank_in_archive_order":53,"model":"TODKAT","metrics":{"Accuracy":"63.4","Macro-F1":"60.66","Weighted-F1":"62.75"},"uses_additional_data":false,"paper_date":"2021-06-02","paper":"/paper/topic-driven-and-knowledge-aware-transformer","paper_url":"https://arxiv.org/abs/2106.01071v1","paper_title":"Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":54,"model":"KET","metrics":{"Micro-F1":"61.11","Weighted-F1":"61.33"},"uses_additional_data":false,"paper_date":"2019-09-24","paper":"/paper/knowledge-enriched-transformer-for-emotion","paper_url":"https://arxiv.org/abs/1909.10681v2","paper_title":"Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations","code":"https://github.com/zhongpeixiang/KET","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":55,"model":"VHRED","metrics":{"Weighted-F1":"59.56"},"uses_additional_data":false,"paper_date":"2019-10-11","paper":"/paper/emotion-recognition-in-conversations-with","paper_url":"https://arxiv.org/abs/1910.04980v3","paper_title":"Conversational Transfer Learning for Emotion Recognition","code":"https://github.com/SenticNet/conv-emotion","n_code_links":1,"syntology":null},{"rank_in_archive_order":56,"model":"ICON","metrics":{"Weighted-F1":"58.6"},"uses_additional_data":false,"paper_date":"2018-10-01","paper":"/paper/icon-interactive-conversational-memory","paper_url":"https://aclanthology.org/D18-1280","paper_title":"ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection","code":"https://github.com/SenticNet/conv-emotion","n_code_links":1,"syntology":null},{"rank_in_archive_order":57,"model":"bc-LSTM+Att","metrics":{"Accuracy":"59.09","Macro-F1":"56.52","Weighted-F1":"58.54"},"uses_additional_data":false,"paper_date":"2017-07-01","paper":"/paper/context-dependent-sentiment-analysis-in-user","paper_url":"https://aclanthology.org/P17-1081","paper_title":"Context-Dependent Sentiment Analysis in User-Generated Videos","code":"https://github.com/soujanyaporia/multimodal-sentiment-analysis","n_code_links":2,"syntology":null},{"rank_in_archive_order":58,"model":"CMN","metrics":{"Accuracy":"56.32","Macro-F1":"54.84","Weighted-F1":"56.19"},"uses_additional_data":false,"paper_date":"2018-06-01","paper":"/paper/conversational-memory-network-for-emotion","paper_url":"https://aclanthology.org/N18-1193","paper_title":"Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos","code":"https://github.com/SenticNet/conv-emotion","n_code_links":1,"syntology":null},{"rank_in_archive_order":59,"model":"SPECTRA","metrics":{"Accuracy":"67.94"},"uses_additional_data":false,"paper_date":"2023-05-19","paper":"/paper/speech-text-dialog-pre-training-for-spoken","paper_url":"https://arxiv.org/abs/2305.11579v2","paper_title":"Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment","code":"https://github.com/alibabaresearch/damo-convai","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":7,"rows_with_any_sample_ran":5,"distinct_papers_with_graph_line":7,"distinct_papers_with_any_sample_ran":5,"samples_over_distinct_papers":{"n_ran":14,"n_unverified":15,"n_samples":29,"n_pointer_only_licence":6,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":14,"n_unverified":15,"n_samples":29,"n_pointer_only_licence":6,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}