Emotion Cause Extraction
13 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications.
RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction
The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document.
End-to-end Emotion-Cause Pair Extraction via Learning to Link
Specifically, our model regards pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i. e., the links are directional.
From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification
We introduce a relative position augmented embedding learning algorithm, and transform the task from an independent prediction problem to a reordered prediction problem, where the dynamic global label information is incorporated.
An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction
We therefore conclude that it is the innate bias in this benchmark that caused high accuracy rate of these deep learning models in ECE.
Recognizing Emotion Cause in Conversations
We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines.
An End-to-End Network for Emotion-Cause Pair Extraction
The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document.
A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness
Existing work follows a two-stage pipeline which identifies emotions and causes at the first step and pairs them at the second step.
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction
To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses.
Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction
Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text.