  Emotion-cause pair extraction (ECPE), as an emergent natural language
processing task, aims at jointly investigating emotions and their underlying
causes in documents. It extends the previous emotion cause extraction (ECE)
task, yet without requiring a set of pre-given emotion clauses as in ECE.
Existing approaches to ECPE generally adopt a two-stage method, i.e., (1)
emotion and cause detection, and then (2) pairing the detected emotions and
causes. Such pipeline method, while intuitive, suffers from two critical
issues, including error propagation across stages that may hinder the
effectiveness, and high computational cost that would limit the practical
application of the method. To tackle these issues, we propose a multi-task
learning model that can extract emotions, causes and emotion-cause pairs
simultaneously in an end-to-end manner. 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. Emotion extraction and cause
extraction are incorporated into the model as auxiliary tasks, which further
boost the pair extraction. Experiments are conducted on an ECPE benchmarking
dataset. The results show that our proposed model outperforms a range of
state-of-the-art approaches.
