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

• URL: http://arxiv.org/abs/2109.04919v1
• Date: Fri, 10 Sep 2021 15:00:01 GMT
• Title: EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion in Task-Oriented Dialogue Systems
• Authors: Shutong Feng, Nurul Lubis, Christian Geishauser, Hsien-chin Lin, Michael Heck, Carel van Niekerk and Milica Ga\v{s}i\'c
• Abstract summary: EmoWOZ is a large-scale manually emotion-annotated corpus of task-oriented dialogues. It contains more than 11K dialogues with more than 83K emotion annotations of user utterances. We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues.
• Score: 3.3010169113961325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 have been largely overlooked despite having an equally important role, such as to signal failure or success. 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 Wizzard-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.

Related papers

• Infusing Emotions into Task-oriented Dialogue Systems: Understanding, Management, and Generation [6.377334634656281]
  Emotions are indispensable in human communication, but are often overlooked in task-oriented dialogue (ToD) modelling. In this work, we incorporate emotion into the complete ToD processing loop, involving understanding, management, and generation. We demonstrate that our proposed framework significantly enhances the user's emotional experience as well as the task success.
  arXiv  Detail & Related papers  (2024-08-05T12:21:04Z)
• Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
  We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN) EDEN recognizes emotion and causes in an explicitly thinking way. It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
  arXiv  Detail & Related papers  (2024-06-07T08:58:29Z)
• SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations [53.60993109543582]
  SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, aims at extracting all pairs of emotions and their corresponding causes from conversations. Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE) In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.
  arXiv  Detail & Related papers  (2024-05-19T09:59:00Z)
• Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
  We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system. We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
  arXiv  Detail & Related papers  (2024-04-03T08:48:50Z)
• Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
  We propose a Dynamic Causal Disentanglement Model based on hidden variable separation. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
  arXiv  Detail & Related papers  (2023-09-13T12:58:09Z)
• Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation [16.659457455269127]
  We propose a two-stage conversational agent for the generation of emotional dialogue. First, a dialogue model trained without the emotion-annotated dialogue corpus generates a prototype response that meets the contextual semantics. Secondly, the first-stage prototype is modified by a controllable emotion refiner with the empathy hypothesis.
  arXiv  Detail & Related papers  (2023-01-12T10:03:56Z)
• CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI [48.67259855309959]
  Most existing datasets for conversational AI ignore human personalities and emotions. We propose CPED, a large-scale Chinese personalized and emotional dialogue dataset. CPED contains more than 12K dialogues of 392 speakers from 40 TV shows.
  arXiv  Detail & Related papers  (2022-05-29T17:45:12Z)
• EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues [44.79509115642278]
  We create a large conversational dataset in Hindi named EmoInHindi for multi-label emotion and intensity recognition in conversations. We prepare our dataset in a Wizard-of-Oz manner for mental health and legal counselling of crime victims.
  arXiv  Detail & Related papers  (2022-05-27T11:23:50Z)
• Generating Empathetic Responses with a Large Scale Dialog Dataset [0.76146285961466]
  Existing models either directly incorporate pre-defined emotion information to guide the response generation, or use deterministic rules to decide the response emotion. We show how to build a multi-turn empathetic dialog model that performs well compared to its baselines over 6,000 human evaluated instances.
  arXiv  Detail & Related papers  (2021-05-14T13:45:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.