  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.
