Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. In this paper, we present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes. Specifically, we design the domain- and target-constrained cloze test, which can leverage the PTLMs' strong language modeling ability to generate the given target's attributes pertaining to the review context. The attributes contain the background and property information of the target, which can help to enrich the semantics of the review context and the target. To exploit the attributes for tackling TSC, we first construct a heterogeneous information graph by treating the attributes as nodes and combining them with (1) the syntax graph automatically produced by the off-the-shelf dependency parser and (2) the semantics graph of the review context, which is derived from the self-attention mechanism. Then we propose a heterogeneous information gated graph convolutional network to model the interactions among the attribute information, the syntactic information, and the contextual information. The experimental results on three benchmark datasets demonstrate the superiority of our model, which achieves new state-of-the-art performance.

翻译：现有的基于PTLM的目标情感分类模型可分为两类：1）采用PTLM作为上下文编码器的微调模型；2）将分类任务转化为文本/单词生成任务的提示模型。本文提出了利用PTLM进行目标情感分类的新视角：通过上下文目标属性同时发挥语言建模与显式目标-上下文交互的优势。具体而言，我们设计了领域与目标约束的完形填空任务，利用PTLM强大的语言建模能力生成与评论上下文相关的给定目标属性。这些属性包含目标的背景与性质信息，有助于丰富评论上下文与目标的语义。为利用属性解决目标情感分类问题，我们首先构建异质信息图：将属性视为节点，并将其与（1）现成依存解析器自动生成的语法图及（2）源自自注意力机制的评论上下文语义图相结合。随后提出异质信息门控图卷积网络，对属性信息、句法信息与上下文信息间的交互进行建模。在三个基准数据集上的实验结果表明，我们提出的模型具有优越性，实现了新的最优性能。