Abstract
The purpose of emotion-cause pair extraction is to extract the pair of emotion clauses and cause clauses. On the one hand, the existing methods do not take fully into account the relationship between the emotion extraction of two auxiliary tasks. On the other hand, the existing two-stage model has the problem of error propagation. In addition, existing models do not adequately address the emotion and cause-induced locational imbalance of samples. To solve these problems, an end-to-end multitasking model (MM-ECPE) based on shared interaction between GRU, knowledge graph and transformer modules is proposed. Furthermore, based on MM-ECPE, in order to use the encoder layer to better solve the problem of imbalanced distribution of clause distances between clauses and emotion clauses, we propose a novel encoding based on BERT, sentiment lexicon, and position-aware interaction module layer of emotion motif pair retrieval model (MM-ECPE(BERT)). The model first fully models the interaction between different tasks through the multi-level sharing module, and mines the shared information between emotion-cause pair extraction and the emotion extraction and cause extraction. Second, to solve the imbalanced distribution of emotion clauses and cause clauses problem, suitable labels are screened out according to the knowledge graph path length and task-specific features are constructed so that the model can focus on extracting pairs with corresponding emotion-cause relationships. Experimental results on the ECPE benchmark dataset show that the proposed model achieves good performance, especially on position-imbalanced samples.
Abstract (translated)
情感词对提取的目的是提取情感短语和原因短语。一方面，现有的方法没有充分考虑两个自辅助任务之间的情感提取关系。另一方面，现有的两阶段模型存在错误传播问题。此外，现有的模型没有充分解决样本情感和原因诱导的局部不平衡问题。为解决这些问题，我们提出了一个基于GRU、知识图和Transformer模块的端到端多任务模型（MM-ECPE）。 此外，基于MM-ECPE，为了更好地利用编码器层解决词汇表征层之间短语距离的不平衡问题，我们提出了一个基于BERT、情感词汇和位置感知交互模块的情感短语对检索模型（MM-ECPE(BERT)）的新编码器层。 模型首先通过多级共享模块全面建模不同任务之间的交互，并挖掘情感词对提取和情感提取及原因提取之间的共享信息。然后，为解决情感短语和原因短语的不平衡分布问题，根据知识图路径长度和任务特定特征筛选出适当的标签，以便模型集中精力提取相应情感词对之间的关系。在ECPE基准数据集的实验结果中，与现有模型相比，所提出的模型在位置不平衡样本上的表现良好。
URL
https://arxiv.org/abs/2404.06812
