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.

翻译：情感-原因派(ECPE)的任务旨在提取所有潜在的情感-原因文件,而没有情感或原因条款的说明。以前关于ECPE的方法试图通过使用复杂的结构来模拟情感-原因互动来改进常规的两步处理计划。在本文中,我们把ECPE的任务放在回答问题(QA)上,并提出简单而有效的基于BERT的解决方案来解决这个问题。根据一份文件,我们的“指导-QA”模型首先用固定的问题来预测最佳情感条款。然后,预测的情绪被用作预测情绪最潜在原因的问题。我们评估了标准的ECPE文体的模型。实验结果显示,尽管我们的指导-质量A很简单,但是它还是取得了很有希望的结果,并且很容易复制。还提供了指导-质量的代码。