  Emotion-cause pair extraction (ECPE) is an emerging task aiming to extract
potential pairs of emotions and corresponding causes from documents. Previous
approaches have focused on modeling the pair-to-pair relationship and achieved
promising results. However, the clause-to-clause relationship, which
fundamentally symbolizes the underlying structure of a document, has still been
in its research infancy. In this paper, we define a novel clause-to-clause
relationship. To learn it applicably, we propose a general clause-level
encoding model named EA-GAT comprising E-GAT and Activation Sort. E-GAT is
designed to aggregate information from different types of clauses; Activation
Sort leverages the individual emotion/cause prediction and the sort-based
mapping to propel the clause to a more favorable representation. Since EA-GAT
is a clause-level encoding model, it can be broadly integrated with any
previous approach. Experimental results show that our approach has a
significant advantage over all current approaches on the Chinese and English
benchmark corpus, with an average of $2.1\%$ and $1.03\%$.
