论文标题
多任务学习框架，用于提取情感原因原因跨度和对话中的需要
Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations
论文作者
论文摘要
在文本中预测情绪是NLP社区中一个充分研究的问题。最近，有积极的研究在提取文本中表达的情绪的原因方面进行了积极研究。以前的大多数工作都在文档中造成了因果情绪。在这项工作中，我们提出了神经模型来提取情感原因跨度和对话中的需求。为了学习此类模型，我们使用REC con数据集，该数据集用在话语层面上的原因跨度注释。特别是，我们提出了MUTEC，这是一个端到端的多任务学习框架，用于提取情绪，情感原因和对话中的努力。这与现有的基线模型相反，后者使用地面真理情绪来提取原因。对于数据集中提供的大多数数据折叠，MUTEC的性能优于基线。
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.
