Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text. The previous works usually extract the emotion-cause pairs from two perspectives of emotion and cause. However, emotion extraction is more crucial to the ECPE task than cause extraction. Motivated by this analysis, we propose an end-to-end emotion-cause extraction approach oriented toward emotion prediction (EPO-ECPE), aiming to fully exploit the potential of emotion prediction to enhance emotion-cause pair extraction. Considering the strong dependence between emotion prediction and emotion-cause pair extraction, we propose a synchronization mechanism to share their improvement in the training process. That is, the improvement of emotion prediction can facilitate the emotion-cause pair extraction, and then the results of emotion-cause pair extraction can also be used to improve the accuracy of emotion prediction simultaneously. For the emotion-cause pair extraction, we divide it into genuine pair supervision and fake pair supervision, where the genuine pair supervision learns from the pairs with more possibility to be emotion-cause pairs. In contrast, fake pair supervision learns from other pairs. In this way, the emotion-cause pairs can be extracted directly from the genuine pair, thereby reducing the difficulty of extraction. Experimental results show that our approach outperforms the 13 compared systems and achieves new state-of-the-art performance.

翻译：情感-原因对抽取任务旨在从无标注的情感文本中抽取所有情感及其原因的对。以往研究通常从情感和原因两个视角抽取情感-原因对。然而，情感抽取对情感-原因对抽取任务比原因抽取更为关键。基于此分析，我们提出了一种面向情感预测的端到端情感-原因抽取方法（EPO-ECPE），旨在充分利用情感预测的潜力以增强情感-原因对抽取。考虑到情感预测与情感-原因对抽取之间的强依赖性，我们提出了一种同步机制，在训练过程中共享二者的改进效果。即情感预测的改进可促进情感-原因对抽取，同时情感-原因对抽取的结果也可用于提升情感预测的准确性。针对情感-原因对抽取，我们将其划分为真实对监督与虚假对监督，其中真实对监督从更可能成为情感-原因的对中学习，而虚假对监督则从其他对中学习。通过这种方式，情感-原因对可直接从真实对中抽取，从而降低抽取难度。实验结果表明，我们的方法超越了13个对比系统，并取得了最新的最优性能。