Syntax-Aware Transformer for Sentence Classification

Jiajun Shan, Zhiqiang Zhang, Yuwei Zeng, Yuyan Ying, Haiyan Wu, Haiyu Song, Yanhong Chen, Shengchun Deng

Abstract: Sentence classification is a significant task in natural language processing (NLP) and is applied in many fields. The syntactic and semantic properties of words and phrases often determine the success of sentence classification. Previous approaches based on sequential modeling mainly ignored the explicit syntactic structures in a sentence. In this paper, we propose a Syntax-Aware Transformer (SA-Trans), which integrates syntactic information in the transformer and obtains sentence embeddings by combining syntactic and semantic information. We evaluate our SA-Trans on four benchmark classification datasets (i.e., AG’News, DBpedia, ARP, ARF), and the experimental results manifest that our SA-Trans model achieves competitive performance compared to the baseline models. Finally, the case study further demonstrates the importance of syntactic information for the classification task.