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 with no code
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition
On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively.
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog
How to model the context in a conversation is a central aspect and a major challenge of ERC tasks.
SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation
Emotion Recognition in Conversation~(ERC) across modalities is of vital importance for a variety of applications, including intelligent healthcare, artificial intelligence for conversation, and opinion mining over chat history.
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition
The audio and text representations are processed using a set of bi-directional recurrent neural network layers with self-attention that converts each utterance in a given conversation to a fixed dimensional embedding.
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation
Accordingly, we propose a novel paradigm, i. e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy.
Deep Emotion Recognition in Textual Conversations: A Survey
This is followed by descriptions of the most prominent works in ERC with explanations of the Deep Learning architectures employed.
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations
The Korean Drama Scene Transcript dataset for Emotion Recognition (KD-EmoR) is a text-based conversation dataset.
Emotion Recognition in Conversation using Probabilistic Soft Logic
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation
Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC.
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation
We introduce a new feature extractor to extract latent features from the audio and visual modality.
