{"url":"/dataset/dailydialog","name":"DailyDialog","full_name":null,"description_markdown":"**DailyDialog** is a high-quality multi-turn open-domain English dialog dataset. It contains 13,118 dialogues split into a training set with 11,118 dialogues and validation and test sets with 1000 dialogues each. On average there are around 8 speaker turns per dialogue with around 15 tokens per turn.\r\n\r\nSource: [http://yanran.li/dailydialog](http://yanran.li/dailydialog)\r\nImage Source: [https://paperswithcode.com/paper/dailydialog-a-manually-labelled-multi-turn/](https://paperswithcode.com/paper/dailydialog-a-manually-labelled-multi-turn/)","description_withheld":null,"homepage":"http://yanran.li/dailydialog","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/dailydialog-a-manually-labelled-multi-turn","title":"DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset","first_author":"Yan-ran Li","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"http://yanran.li/dailydialog"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Dialog","url":"/datasets/modality/dialog"}],"tasks":[{"name":"Text Generation","url":"/task/text-generation","datasets_with_task":"/datasets/task/text-generation"},{"name":"Emotion Recognition in Conversation","url":"/task/emotion-recognition-in-conversation","datasets_with_task":"/datasets/task/emotion-recognition-in-conversation"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["DailyDialog"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/li2017dailydialog/daily_dialog","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/daily_dialog","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/facebookresearch/ParlAI","url":"https://parl.ai/docs/tasks.html#daily-dialog","frameworks":["pytorch"]}],"num_papers_in_archive":399,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/emotion-recognition-in-conversation-on-3","task":"Emotion Recognition in Conversation","dataset_variant":"DailyDialog","rows":22,"metrics":["Micro-F1","Macro F1","Weighted F1"],"first_row_in_archive_order":{"model":"S+PAGE","paper":"/paper/s-page-a-speaker-and-position-aware-graph","metrics":{"Micro-F1":"64.07"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/text-generation-on-dailydialog","task":"Text Generation","dataset_variant":"DailyDialog","rows":1,"metrics":["BLEU-1","BLEU-2","BLEU-3","BLEU-4"],"first_row_in_archive_order":{"model":"AEM+Attention","paper":"/paper/an-auto-encoder-matching-model-for-learning","metrics":{"BLEU-1":"14.17","BLEU-2":"5.69","BLEU-3":"3.78","BLEU-4":"2.84"},"code_links":[{"title":"lancopku/AMM","url":"https://github.com/lancopku/AMM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/accumulating-word-representations-in-multi","title":"Accumulating Word Representations in Multi-level Context Integration for ERC Task","date":"2023-11-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fuzzy-fingerprinting-transformer-language","title":"Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations","date":"2023-09-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/context-dependent-embedding-utterance","title":"Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations","date":"2023-04-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/emotionic-emotional-inertia-and-contagion","title":"EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation","date":"2023-03-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/the-emotion-is-not-one-hot-encoding-learning","title":"The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation","date":"2022-06-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/s-page-a-speaker-and-position-aware-graph","title":"S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation","date":"2021-12-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hybrid-curriculum-learning-for-emotion","title":"Hybrid Curriculum Learning for Emotion Recognition in Conversation","date":"2021-12-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/contrast-and-generation-make-bart-a-good","title":"Contrast and Generation Make BART a Good Dialogue Emotion Recognizer","date":"2021-12-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/past-present-and-future-conversational","title":"Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge","date":"2021-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/knowledge-interactive-network-with-sentiment","title":"Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations","date":"2021-11-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/graph-based-network-with-contextualized","title":"Graph Based Network with Contextualized Representations of Turns in Dialogue","date":"2021-09-09","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":10,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/compm-context-modeling-with-speaker-s-pre","title":"CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation","date":"2021-08-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/topic-driven-and-knowledge-aware-transformer","title":"Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection","date":"2021-06-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/directed-acyclic-graph-network-for","title":"Directed Acyclic Graph Network for Conversational Emotion Recognition","date":"2021-05-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dialogxl-all-in-one-xlnet-for-multi-party","title":"DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition","date":"2020-12-16","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/relation-aware-graph-attention-networks-with","title":"Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations","date":"2020-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cosmic-commonsense-knowledge-for-emotion","title":"COSMIC: COmmonSense knowledge for eMotion Identification in Conversations","date":"2020-10-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hierarchical-pre-training-for-sequence","title":"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog","date":"2020-09-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/contextualized-emotion-recognition-in","title":"Contextualized Emotion Recognition in Conversation as Sequence Tagging","date":"2020-07-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/emotion-recognition-in-conversations-with","title":"Conversational Transfer Learning for Emotion Recognition","date":"2019-10-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/knowledge-enriched-transformer-for-emotion","title":"Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations","date":"2019-09-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/an-auto-encoder-matching-model-for-learning","title":"An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation","date":"2018-08-27","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":26,"samples_ran":16,"samples_unverified":10,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}