Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion cause span and entailment in conversations. For learning such models, we use RECCON dataset, which is annotated with cause spans at the utterance level. In particular, we propose MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment in conversations. This is in contrast to existing baseline models that use ground truth emotions to extract the cause. MuTEC performs better than the baselines for most of the data folds provided in the dataset.

翻译：在文本中表达的预测情感是国家语言平台社区中一个研究周密的问题。 最近,在提取文本中表达的情感原因方面开展了积极研究。 先前的大部分工作在文件中做了因果情感因素。 在这项工作中,我们提出了神经模型,以提取情感引发的跨度和在对话中引起的影响。 为了学习这些模型,我们使用了RECCON数据集,该数据集在语句中带有因果跨度附加说明。特别是,我们提出了MUTEC,这是一个用于提取情感、情感原因和对话的端到端多任务学习框架。这与利用现有基线模型使用地面真相情感来提取原因的模型不同。 MuTEC比数据集中提供的大多数数据折叠的基线要好。