论文标题
基于成对的关节编码与情感双提取的关系图卷积网络
Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction
论文作者
论文摘要
情绪原因对提取（ECPE）旨在提取情感条款和相应的原因条款，这些条款最近受到了人们的关注。先前的方法将特征依次编码指定顺序。他们首先编码情感并引起子句提取的特征，然后将它们组合在一起以进行配对。这会导致任务间特征交互的不平衡，稍后提取的功能与前者没有直接接触。为了解决这个问题，我们提出了一个基于成对的联合编码（PBJE）网络，该网络以联合特征编码方式同时生成对和子句特征，以模拟条款中的因果关系。 PBJE可以平衡情感条款之间的信息流，导致子句和对。从多关系的角度来看，我们构建了一个异质的无向图，并应用关系图卷积网络（RGCN）来捕获条款与对与条款之间的关系之间的各种关系。实验结果表明，PBJE在中国基准语料库上实现了最先进的表现。
Emotion-cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses, which have recently received growing attention. Previous methods sequentially encode features with a specified order. They first encode the emotion and cause features for clause extraction and then combine them for pair extraction. This lead to an imbalance in inter-task feature interaction where features extracted later have no direct contact with the former. To address this issue, we propose a novel Pair-Based Joint Encoding (PBJE) network, which generates pairs and clauses features simultaneously in a joint feature encoding manner to model the causal relationship in clauses. PBJE can balance the information flow among emotion clauses, cause clauses and pairs. From a multi-relational perspective, we construct a heterogeneous undirected graph and apply the Relational Graph Convolutional Network (RGCN) to capture the various relationship between clauses and the relationship between pairs and clauses. Experimental results show that PBJE achieves state-of-the-art performance on the Chinese benchmark corpus.
