Abstract
The emotion cause extraction (ECE) task aims at
discovering the potential causes behind a certain
emotion expression in a document. Techniques
including rule-based methods, traditional machine
learning methods and deep neural networks have
been proposed to solve this task. However, most
of the previous work considered ECE as a set of
independent clause classification problems and ignored the relations between multiple clauses in
a document. In this work, we propose a joint
emotion cause extraction framework, named RNNTransformer Hierarchical Network (RTHN), to encode and classify multiple clauses synchronously.
RTHN is composed of a lower word-level encoder
based on RNNs to encode multiple words in each
clause, and an upper clause-level encoder based
on Transformer to learn the correlation between
multiple clauses in a document. We furthermore
propose ways to encode the relative position and
global predication information into Transformer
that can capture the causality between clauses and
make RTHN more efficient. We finally achieve the
best performance among 12 compared systems and
improve the F1 score of the state-of-the-art from
72.69% to 76.77%