Emotion Recognition in Conversation
72 papers with code • 12 benchmarks • 14 datasets
Given the transcript of a conversation along with speaker information of each constituent utterance, the ERC task aims to identify the emotion of each utterance from several pre-defined emotions. Formally, given the input sequence of N number of utterances [(u1, p1), (u2, p2), . . . , (uN , pN )], where each utterance ui = [ui,1, ui,2, . . . , ui,T ] consists of T words ui,j and spoken by party pi, the task is to predict the emotion label ei of each utterance ui. .
Benchmarks
These leaderboards are used to track progress in Emotion Recognition in Conversation
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Libraries
Use these libraries to find Emotion Recognition in Conversation models and implementations
IEMOCAP
DailyDialog
MELD
EmoryNLP
SEMAINE
EmotionLines
EmoContext
CPED
CMU-MOSI
EmoWOZ
Latest papers
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
yu-fangxu/eacl
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To achieve this, we utilize label encodings as anchors to guide the learning of utterance representations and design an auxiliary loss to ensure the effective separation of anchors for similar emotions.
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition
Emotion recognition in conversation (ERC) is a crucial task in natural language processing and affective computing.
TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation
yuntaeyang/telme
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In this paper, we propose Teacher-leading Multimodal fusion network for ERC (TelME).
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
alibaba-damo-academy/FunASR
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Recently, instruction-following audio-language models have received broad attention for audio interaction with humans.
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations
Emotion recognition in conversations (ERC), the task of recognizing the emotion of each utterance in a conversation, is crucial for building empathetic machines.
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions.
InstructERC: Reforming Emotion Recognition in Conversation with a Retrieval Multi-task LLMs Framework
The field of emotion recognition of conversation (ERC) has been focusing on separating sentence feature encoding and context modeling, lacking exploration in generative paradigms based on unified designs.
UniSA: Unified Generative Framework for Sentiment Analysis
dawn0815/UniSA
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Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information.
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition
This module can construct the interaction between different modalities and capture long-range contextual information based on similarity clusters.
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition
RUME is applied to extract conversation-level contextual emotional cues while pulling together data distributions between modalities; ACME is utilized to perform multimodal interaction centered on textual modality; LESM is used to model emotion shift and capture emotion-shift information, thereby guiding the learning of the main task.
