Emotion Recognition in Conversation

73 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. .


Use these libraries to find Emotion Recognition in Conversation models and implementations

Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

google-research/bert NAACL 2019

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

Convolutional Neural Networks for Sentence Classification

PaddlePaddle/PaddleNLP EMNLP 2014

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.

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

bioinf-jku/TTUR NeurIPS 2017

Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.

Bag of Tricks for Efficient Text Classification

facebookresearch/fastText EACL 2017

This paper explores a simple and efficient baseline for text classification.

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

declare-lab/MELD ACL 2019

We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.

DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition

shenwzh3/DialogXL 16 Dec 2020

Specifically, we first modify the recurrence mechanism of XLNet from segment-level to utterance-level in order to better model the conversational data.

Structure-Aware Transformer for Graph Representation Learning

borgwardtlab/sat 7 Feb 2022

Here, we show that the node representations generated by the Transformer with positional encoding do not necessarily capture structural similarity between them.

Context-Dependent Sentiment Analysis in User-Generated Videos

senticnet/sc-lstm ACL 2017

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

SenticNet/conv-emotion 1 Nov 2018

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

SenticNet/conv-emotion IJCNLP 2019

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.