  Emotion cause analysis such as emotion cause extraction (ECE) and
emotion-cause pair extraction (ECPE) have gradually attracted the attention of
many researchers. However, there are still two shortcomings in the existing
research: 1) In most cases, emotion expression and cause are not the whole
clause, but the span in the clause, so extracting the clause-pair rather than
the span-pair greatly limits its applications in real-world scenarios; 2) It is
not enough to extract the emotion expression clause without identifying the
emotion categories, the presence of emotion clause does not necessarily convey
emotional information explicitly due to different possible causes. In this
paper, we propose a new task: Emotion-Cause Span-Pair extraction and
classification (ECSP), which aims to extract the potential span-pair of emotion
and corresponding causes in a document, and make emotion classification for
each pair. In the new ECSP task, ECE and ECPE can be regarded as two special
cases at the clause-level. We propose a span-based extract-then-classify (ETC)
model, where emotion and cause are directly extracted and paired from the
document under the supervision of target span boundaries, and corresponding
categories are then classified using their pair representations and localized
context. Experiments show that our proposed ETC model outperforms the SOTA
model of ECE and ECPE task respectively and gets a fair-enough results on ECSP
task.
