SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations

• URL: http://arxiv.org/abs/2405.13049v3
• Date: Mon, 8 Jul 2024 07:32:28 GMT
• Title: SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations
• Authors: Fanfan Wang, Heqing Ma, Jianfei Yu, Rui Xia, Erik Cambria,
• Abstract summary: 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.
• Score: 53.60993109543582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social interactions. In addition to emotion recognition in conversations, the task of identifying the potential causes behind an individual's emotional state in conversations, is of great importance in many application scenarios. We organize SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, which 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). The shared task has attracted 143 registrations and 216 successful submissions. 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.

Related papers

• 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)
• Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations [12.095837596104552]
  In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. New task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. We propose a multi-stage framework to generate emotion and extract the emotion causal pairs given the target emotion.
  arXiv  Detail & Related papers  (2024-04-25T11:52:21Z)
• 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)
• LastResort at SemEval-2024 Task 3: Exploring Multimodal Emotion Cause Pair Extraction as Sequence Labelling Task [3.489826905722736]
  SemEval 2024 introduces the task of Multimodal Emotion Cause Analysis in Conversations. This paper proposes models that tackle this task as an utterance labeling and a sequence labeling problem. In the official leaderboard for the task, our architecture was ranked 8th, achieving an F1-score of 0.1759 on the leaderboard.
  arXiv  Detail & Related papers  (2024-04-02T16:32:49Z)
• SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF) [61.49972925493912]
  SemEval-2024 Task 10 is a shared task centred on identifying emotions in code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for code-mixed dialogues, emotion flip reasoning for code-mixed dialogues, and emotion flip reasoning for English dialogues. A total of 84 participants engaged in this task, with the most adept systems attaining F1-scores of 0.70, 0.79, and 0.76 for the respective subtasks.
  arXiv  Detail & Related papers  (2024-02-29T08:20:06Z)
• 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)
• 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)
• Multimodal Emotion-Cause Pair Extraction in Conversations [23.95461291718006]
  We introduce a new task named Multimodal Emotion-Cause Pair Extraction in Conversations. We aim to jointly extract emotions and their associated causes from conversations reflected in texts, audio and video. Preliminary experimental results demonstrate the potential of multimodal information fusion for discovering both emotions and causes in conversations.
  arXiv  Detail & Related papers  (2021-10-15T11:30:24Z)
• EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion in Task-Oriented Dialogue Systems [3.3010169113961325]
  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.
  arXiv  Detail & Related papers  (2021-09-10T15:00:01Z)
• Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies [106.62835060095532]
  We discuss several key aspects of multi-modal emotion recognition (MER) We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks. Finally, we outline several real-world applications and discuss some future directions.
  arXiv  Detail & Related papers  (2021-08-18T21:55:20Z)

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.