DCN-ECPE: Dual-Channel Network for Emotion-Cause Pair Extraction

Pei Qie, Kai Shuang
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引用次数: 1

Abstract

It is a challenge task to extract the potential pairs of emotion clause and corresponding cause clause from the documents. The existing state-of-the-art ECPE method formulates the task in an end-to-end model, which processes the interactions of emotion-cause pairs based on joint two-dimensional. The model has two shortcomings: 1) the potential semantic particularity of the causal relation between the emotion-cause pair is not fully considered; 2) it falls short of capturing various regional features of contextualized representation. In this work, we propose an end-to-end model named DCN-ECPE. The model generates the representation of emotion-cause pairs with dual-channel, which takes both potential causal features and contextualized interactions of the clause pairs into consideration. One channel extracts potential semantic feature of the causal relation from constructed statements, and the other channel processes the representation of clause pairs with CNN to capture various regional features. Our method outperforms existing state-of-the-art end-to-end ECPE method in all aspects.
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DCN-ECPE:情感原因对提取的双通道网络
从文档中提取情感子句和相应的原因子句是一项具有挑战性的任务。现有的最先进的ECPE方法在端到端模型中制定任务,该模型处理基于关节二维的情绪-原因对的相互作用。该模型存在两个不足:1)没有充分考虑情感-原因对因果关系潜在的语义特殊性;2)未能捕捉到语境化表征的各种地域特征。在这项工作中,我们提出了一个端到端模型,命名为DCN-ECPE。该模型既考虑了潜在的因果特征,又考虑了子句对的情境化交互作用,生成了双通道的情感-原因对表示。一个通道从构建的语句中提取因果关系的潜在语义特征,另一个通道使用CNN处理子句对的表示以捕获各种区域特征。我们的方法在各个方面都优于现有的最先进的端到端ECPE方法。
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