Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a document.It generally contains three subtasks,emotions extraction,causes extraction,and causal relations detec...Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a document.It generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and causes.Existing works adopt pipelined approaches or multi-task learning to address the ECPE task.However,the pipelined approaches easily suffer from error propagation in real-world scenarios.Typical multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction results.To address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging task.Unlike prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging task.Through this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause pairs.To validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark dataset.The experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.展开更多
情绪原因对抽取任务是将情绪子句与原因子句同时抽取。针对该任务,现有模型的编码层未考虑强化情感词语义表示,且仅使用单一图注意力网络,因此,该文提出了一个使用情感词典、图网络和多头注意力的情绪原因对抽取方法(Sen-BiGAT-Inter)...情绪原因对抽取任务是将情绪子句与原因子句同时抽取。针对该任务,现有模型的编码层未考虑强化情感词语义表示,且仅使用单一图注意力网络,因此,该文提出了一个使用情感词典、图网络和多头注意力的情绪原因对抽取方法(Sen-BiGAT-Inter)。该方法首先利用情感词典与子句中的情感词汇匹配,并将匹配的情感词汇与该子句进行合并,再使用预训练模型BERT(Bidirectional Encoder Representation from Transformers)对句子进行表示。其次,建立两个图注意力网络,分别学习情绪子句和原因子句表示,进而获取候选情绪原因对的表示。在此基础上,应用多头注意力交互机制学习候选情绪原因对的全局信息,同时结合相对位置信息得到候选情绪原因对的表示,用于实现情绪原因对的抽取。在中文情绪原因对抽取数据集上的实验结果显示,相比目前最优的结果,该文所提出的模型在F;值上提升约1.95。展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.61976114 and 61936012)the National Key R&D Program of China(2018YFB1005102).
文摘Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a document.It generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and causes.Existing works adopt pipelined approaches or multi-task learning to address the ECPE task.However,the pipelined approaches easily suffer from error propagation in real-world scenarios.Typical multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction results.To address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging task.Unlike prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging task.Through this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause pairs.To validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark dataset.The experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.
文摘情绪原因对抽取任务是将情绪子句与原因子句同时抽取。针对该任务,现有模型的编码层未考虑强化情感词语义表示,且仅使用单一图注意力网络,因此,该文提出了一个使用情感词典、图网络和多头注意力的情绪原因对抽取方法(Sen-BiGAT-Inter)。该方法首先利用情感词典与子句中的情感词汇匹配,并将匹配的情感词汇与该子句进行合并,再使用预训练模型BERT(Bidirectional Encoder Representation from Transformers)对句子进行表示。其次,建立两个图注意力网络,分别学习情绪子句和原因子句表示,进而获取候选情绪原因对的表示。在此基础上,应用多头注意力交互机制学习候选情绪原因对的全局信息,同时结合相对位置信息得到候选情绪原因对的表示,用于实现情绪原因对的抽取。在中文情绪原因对抽取数据集上的实验结果显示,相比目前最优的结果,该文所提出的模型在F;值上提升约1.95。
