Err

Err

Related papers

• AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis [0.6465251961564605]
  The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. Subtask 2 extends the analysis to encompass multimodal cues, including language, audio, and vision, acknowledging instances where causes may not be exclusively represented in the textual data.
  arXiv  Detail & Related papers  (2025-01-19T21:16:31Z)
• EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding [5.3848462080869215]
  We introduce Emotion Universe (EmoVerse), an MLLM designed to handle a broad spectrum of sentiment and emotion-related tasks. EmoVerse is capable of deeply analyzing the underlying causes of emotional states. We also introduce the Affective Multitask (AMT) dataset.
  arXiv  Detail & Related papers  (2024-12-11T02:55:00Z)
• EmoLLM: Multimodal Emotional Understanding Meets Large Language Models [61.179731667080326]
  Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks. But their ability to interpret subjective, emotionally nuanced multimodal content remains largely unexplored. EmoLLM is a novel model for multimodal emotional understanding, incorporating with two core techniques.
  arXiv  Detail & Related papers  (2024-06-24T08:33:02Z)
• 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)
• UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts [64.02363948840333]
  UMETTS is a novel framework that leverages emotional cues from multiple modalities to generate highly expressive and emotionally resonant speech. EP-Align employs contrastive learning to align emotional features across text, audio, and visual modalities, ensuring a coherent fusion of multimodal information. EMI-TTS integrates the aligned emotional embeddings with state-of-the-art TTS models to synthesize speech that accurately reflects the intended emotions.
  arXiv  Detail & Related papers  (2024-04-29T03:19:39Z)
• 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)
• 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)
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli [53.53886609012119]
  We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli. Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
  arXiv  Detail & Related papers  (2023-07-14T00:57:12Z)
• 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.