# EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems

arXiv: https://arxiv.org/abs/2109.04919 · HTML: https://mlsanity.com/paper/2109.04919/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 64 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://mlsanity.com/paper/2005.02877/) (2020)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [Continual Learning in Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2012.15504/) (2020)
- [Recognizing Emotion Cause in Conversations](https://mlsanity.com/paper/2012.11820/) (2020)
- [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017)
- [Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models](https://mlsanity.com/paper/1902.08858/) (2019)
- [ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems](https://mlsanity.com/paper/2002.04793/) (2020)
- [Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking](https://mlsanity.com/paper/2104.04466/) (2021)
- [Sentiment Adaptive End-to-End Dialog Systems](https://mlsanity.com/paper/1804.10731/) (2018)
- [A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents](https://mlsanity.com/paper/1909.04387/) (2019)
- [Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2009.09781/) (2020)
- [Dialogue manager domain adaptation using Gaussian process reinforcement learning](https://mlsanity.com/paper/1609.02846/) (2016)
- [Out-of-Task Training for Dialog State Tracking Models](https://mlsanity.com/paper/2011.09379/) (2020)

## Built on by (most cited first, 20 of 20)

- [ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format](https://mlsanity.com/paper/2211.17148/) (2022)
- [UniSA: Unified Generative Framework for Sentiment Analysis](https://mlsanity.com/paper/2309.01339/) (2023)
- [Towards Online Multi-Modal Social Interaction Understanding](https://mlsanity.com/paper/2503.19851/) (2025)
- [Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence](https://mlsanity.com/paper/2402.10073/) (2024)
- [EmoUS: Simulating User Emotions in Task-Oriented Dialogues](https://mlsanity.com/paper/2306.01579/) (2023)
- [Affect Recognition in Conversations Using Large Language Models](https://mlsanity.com/paper/2309.12881/) (2023)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [Omni-MMSI: Toward Identity-attributed Social Interaction Understanding](https://mlsanity.com/paper/2604.00267/) (2026)
- [Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations](https://mlsanity.com/paper/2304.08216/) (2023)
- [How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning](https://mlsanity.com/paper/2305.02615/) (2023)
- [From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue](https://mlsanity.com/paper/2308.12648/) (2023)
- [A Unified Approach to Emotion Detection and Task-Oriented Dialogue Modeling](https://mlsanity.com/paper/2401.13789/) (2024)
- [Reevaluating Data Partitioning for Emotion Detection in EmoWOZ](https://mlsanity.com/paper/2303.13364/) (2023)
- [Socio-Emotional Response Generation: A Human Evaluation Protocol for LLM-Based Conversational Systems](https://mlsanity.com/paper/2412.04492/) (2024)
- [textttDIAMONDs: A Dataset for mathbbDynamic mathbbInformation mathbbAnd mathbbMental modeling mathbbOf mathbbNumeric mathbbDiscussions](https://mlsanity.com/paper/2505.12651/) (2025)
- [A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction](https://mlsanity.com/paper/2603.03327/) (2026)
- [Evaluating multimodal emotion recognition in proactive conversational agents: A user study](https://mlsanity.com/paper/2605.20200/) (2026)
- [SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark](https://mlsanity.com/paper/2607.18109/) (2026)
- [A Safety-Gated Multimodal AI Backend for Mental-Health Support: Hierarchical State Representation, Conservative Risk Fusion, and Controlled Generation in Anian](https://mlsanity.com/paper/2608.26162/) (2026)
- [Lost with a Map: Conversational State and Behavioral Reliability in Language Models](https://mlsanity.com/paper/2609.33883/) (2026)

## Abstract (arXiv)

The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues remain largely unaddressed. This is despite emotions and dialogue success having equally important roles in a natural system. Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. To lay a foundation for studies on emotions in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than 11K dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. To the best of our knowledge, this is the first large-scale open-source corpus of its kind. We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues. We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.