Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.
翻译:跨部情绪分类( CDSC) 旨在使用源域所学可转让语义, 以预测在未贴标签的目标域中进行审评的情绪。 本任务的现有研究更加关注句号的顺序建模模式,同时基本上忽略图形结构( 即语音标记部分和依赖关系) 中所含的丰富的域异性语义。 作为探索语言理解特性的一个重要方面, 适应性图形表达方式近年来发挥了不可或缺的作用。 为此, 在文件中, 我们的目标是探索从CDSC的图形类结构中学习异性语义特征的可能性。 具体地说, 我们展示了“ 图表调整性语义转移( GAST) 模型, 这是一种适应性合成图形嵌入方法, 能够从文字序列和同步性图表中学习域异性语义语义的语义。 更具体地说, 我们首先提出一个POS- 变式模块, 从单词序列中提取序列中的序列以及基于 spech 的标签。 然后, 我们设计了一个可移动性公共关系中的一种可比较性模块, 。 我们设计了一种可移动性定义 。