Emotion-Cause Pair Extraction
19 papers with code • 2 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.
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.
ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction
In recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis.
Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction
Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document.
Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction
Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text.
A Symmetric Local Search Network for Emotion-Cause Pair Extraction
Each subnetwork is composed of a clause representation learner and a local pair searcher.
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network
Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages.
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.
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature.