  The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all
potential emotion-cause pairs of a document without any annotation of emotion
or cause clauses. Previous approaches on ECPE have tried to improve
conventional two-step processing schemes by using complex architectures for
modeling emotion-cause interaction. In this paper, we cast the ECPE task to the
question answering (QA) problem and propose simple yet effective BERT-based
solutions to tackle it. Given a document, our Guided-QA model first predicts
the best emotion clause using a fixed question. Then the predicted emotion is
used as a question to predict the most potential cause for the emotion. We
evaluate our model on a standard ECPE corpus. The experimental results show
that despite its simplicity, our Guided-QA achieves promising results and is
easy to reproduce. The code of Guided-QA is also provided.

Computation and Language