|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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.
Ranked #5 on Emotion-Cause Pair Extraction on ECPE
The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document.
Ranked #1 on Emotion Cause Extraction on ECE
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.
Ranked #4 on Emotion-Cause Pair Extraction on ECPE
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.
Ranked #2 on Emotion Cause Extraction on ECE
The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document.
Existing work follows a two-stage pipeline which identifies emotions and causes at the first step and pairs them at the second step.
We therefore conclude that it is the innate bias in this benchmark that caused high accuracy rate of these deep learning models in ECE.