Emotion recognition in conversation
Sub-field of emotion recognition
- 1 min read
- 4 chapters
Related on Lably
Through Emotion recognition, Emotion and Dialogue
The task
The task of ERC deals with detecting emotions expressed by the speakers in each utterance of the conversation. ERC depends on three primary factors – the conversational context, interlocutors' mental state, and intent.
Sign in to keep reading
3 more chapters are free with a Lably account.
Datasets
IEMOCAP, SEMAINE, DailyDialogue, and MELD are the four widely used datasets in ERC. Among these four datasets, MELD contains multiparty dialogues.
Methods
Approaches to ERC consist of unsupervised, semi-unsupervised, and supervised methods. Popular supervised methods include using or combining pre-defined features, recurrent neural networks (DialogueRNN), graph convolutional networks (DialogueGCN ), and attention gated hierarchical memory network. Most of the contemporary methods for ERC are deep learning based and rely on the idea of latent speaker-state modeling.
Emotion Cause Recognition in Conversation
Recently a new subtask of ERC has emerged that focuses on recognising emotion cause in conversation. Methods to solve this task rely on language models-based question answering mechanism. RECCON is one of the key datasets for this task.