Emotion Recognition in Conversation
51 papers with code • 11 benchmarks • 12 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. .
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Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Convolutional Neural Networks for Sentence Classification
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
Bag of Tricks for Efficient Text Classification
This paper explores a simple and efficient baseline for text classification.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
Context-Dependent Sentiment Analysis in User-Generated Videos
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines.
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks
We propose a new approach, Knowledge Distillation using Optimal Transport (KNOT), to distill the natural language semantic knowledge from multiple teacher networks to a student network.