  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 RNN-Transformer
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\%.
