An End-to-End Network for Emotion-Cause Pair Extraction

TL;DR

This paper introduces an end-to-end neural network model for Emotion-Cause Pair Extraction, significantly improving performance over previous multi-stage methods and establishing a new baseline on adapted datasets.

Contribution

The paper presents the first end-to-end model for ECPE, eliminating the need for separate emotion and cause extraction stages.

Findings

Achieved approximately 6.5 F1 score increase over multi-stage approaches.

Established a baseline for ECPE on the adapted NTCIR-13 ECE corpus.

Performed comparably to state-of-the-art methods.

Abstract

The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have either followed a multi-stage approach where emotion extraction, cause extraction, and pairing are done independently or use complex architectures to resolve its limitations. In this paper, we propose an end-to-end model for the ECPE task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset. On this dataset, the proposed method produces significant performance improvements (~6.5 increase in F1 score) over the multi-stage approach and achieves comparable performance to the state-of-the-art methods.