DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

By Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, Shuzi Niu. 2017

1,534 citations recorded by the source corpus.

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on http://yanran.li/dailydialog

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions (2020, methodology): “, 2016), BLEU (Papineni et al., 2002; Li et al., 2017c), ROUGE (Lin, 2004; Fan et al.”
Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models (2020, methodology): “For open domain dialogue response generation task, we use the Dailydialog dataset (Li et al. 2017b), which consists of dialogues that resemble daily conversations across multiple topics.”
Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization (2022, methodology): “DailyDialog (Li et al., 2017) Chitchat Dialogue Dialogue History A conversational response METEOR; Learned Intent Classifier BertScore”
Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills (2020, background): “…conversational agents along speciﬁc axes such as how knowledgeable they are (Dinan et al., 2019b; Moghe et al., 2018; Qin et al., 2019), how well they can display empathy (Rashkin et al., 2019; Lin et al., 2019) or talk about their personal background (Zhang et al., 2018; Li et al., 2017).”
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset (2018, methodology): “Another interesting resource is the D AI - LY D IALOG (DD) dataset (Li et al., 2017), which comprises about 13k dialogues obtained by crawling educational websites intended for learners of English and also has emotion label annotations.”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, background): “Researchers have also proposed training dialogue systems online through live interaction with humans, and offline using user simulator models and reinforcement learning methods (Levin et al., 1997; Georgila et al., 2006; Paek, 2006; Schatzmann et al., 2007; Jung et al., 2009; Schatzmann and Young, 2”
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (2019, background): “DailyDialog Li et al. (2017) built a dialogue dataset intended to reﬂect conversations occurring in daily life.”
Challenges in Building Intelligent Open-domain Dialog Systems (2019, background): “DailyDialog [66]: This corpus contains multi-turn dialogs on daily life topics.”
Deploying Lifelong Open-Domain Dialogue Learning (2020, background): “A number of crowdsourced or scraped datasets have been developed to that end, including Daily Dialogue (Li et al., 2017), Per-sonaChat (Li et al., 2016a), Empathetic Dialogues (Rashkin et al., 2019) and Wizard of Wikipedia (Dinan et al., 2019c).”
A Contrastive Framework for Neural Text Generation (2022, methodology): “For the English Benchmark, we use the DailyDialog dataset [15].”
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020, background): “…in the past few years due to the proliferation of publicly available conversational dataset (Poria et al., 2019a; Chatterjee et al., 2019; Li et al., 2017; Zhou et al., 2018a) and its widespread applications in opinion mining, recommender systems, emotion-aware dialogues generation, and…”
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020, methodology): “Dialog response generation We consider Dailydialog (Li et al., 2017c) used in (Gu et al., 2019), which has 13,118 daily conversations.”
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight (2020, methodology): “When reviewing DailyDialog (Li et al., 2017), we find that only 12% conversation samples are of relatively high queryrelatedness scores (> 0.”
M2-CTTS: End-to-End Multi-Scale Multi-Modal Conversational Text-to-Speech Synthesis (2023, methodology): “We use public English corpus DailyTalk [16] to conduct our experiments.”
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020, methodology): “, 2019a) iii) DailyDialog (Li et al., 2017), and iv) EmoryNLP (Zahiri and Choi, 2018).”
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation (2022, background): “Empowering machines to converse like humans is a long-cherished goal of AI community, and there is growing interest in developing open-domain conversational agents (Li et al., 2017; Gao et al., 2018; Ghazvininejad et al., 2018; Zhou et al., 2018a).”
Transformer models for text-based emotion detection: a review of BERT-based approaches (2021, methodology): “The Daily dialogue dataset (Li et al. 2017) was built by crawling dialogues from regular human conversations.”
CIDER: Commonsense Inference for Dialogue Explanation and Reasoning (2021, methodology): “The annotation is performed on the following datasets containing dyadic dialogues: DailyDialog (Li et al., 2017) is aimed towards emotion and dialogue-act classiﬁcation at utterance level.”