From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification

by   Zixiang Ding, et al.

Emotion cause identification aims at identifying the potential causes that lead to a certain emotion expression in text. Several techniques including rule based methods and traditional machine learning methods have been proposed to address this problem based on manually designed rules and features. More recently, some deep learning methods have also been applied to this task, with the attempt to automatically capture the causal relationship of emotion and its causes embodied in the text. In this work, we find that in addition to the content of the text, there are another two kinds of information, namely relative position and global labels, that are also very important for emotion cause identification. To integrate such information, we propose a model based on the neural network architecture to encode the three elements (i.e., text content, relative position and global label), in an unified and end-to-end fashion. 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. Experimental results on a benchmark emotion cause dataset show that our model achieves new state-of-the-art performance and performs significantly better than a number of competitive baselines. Further analysis shows the effectiveness of the relative position augmented embedding learning algorithm and the reordered prediction mechanism with dynamic global labels.


page 1

page 2

page 3

page 4


RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction

The emotion cause extraction (ECE) task aims at discovering the potentia...

An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction

Emotion Cause Extraction (ECE) aims to identify emotion causes from a do...

Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction

The Emotion Cause Extraction (ECE) task aims to identify clauses which c...

A Question Answering Approach to Emotion Cause Extraction

Emotion cause extraction aims to identify the reasons behind a certain e...

Recognizing Emotion Cause in Conversations

Recognizing the cause behind emotions in text is a fundamental yet under...

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

Causal Emotion Entailment (CEE) aims to discover the potential causes be...

Cause Identification from Aviation Safety Incident Reports via Weakly Supervised Semantic Lexicon Construction

The Aviation Safety Reporting System collects voluntarily submitted repo...

Please sign up or login with your details

Forgot password? Click here to reset