Sentence Modeling via Graph Construction and Graph Neural Networks for Semantic Textual Similarity
Ke Zhou, Ke Xu, Tanfeng Sun, Yueguo Zhang · 2020
Recently, using graph neural networks to model the hidden features of natural language has achieved success. In this paper, a novel sentence modeling method named TextSimGNN based on graphical representation is proposed to measure the semantic textual similarity. For embedding sentences into a graphical structure, we first construct a semantic textual graph which combines textual structure information and semantic information together. Then an end-to-end graph neural network is used to measure the similarity between graph pairs. The experiments show that our method has achieved good performance in semantic textual similarity task, which proves the advantage and effectiveness of graphical representation on natural language sentence modeling.