Incorporating external knowledge for text matching model

Text matching is a computational task that involves comparing and establishing the semantic relationship between two textual inputs. The prevailing approach in text matching entails the computation of textual representations or employing attention mechanisms to facilitate interaction with the text. These techniques have demonstrated notable efficacy in various text-matching scenarios. However, these methods primarily focus on modeling the sentence pairs themselves and rarely incorporate additional information to enrich the models. In this study, we address the challenge of text matching in natural language processing by proposing a novel approach that leverages external knowledge sources, namely Wiktionary for word definitions and a knowledge graph for text triplet information. Unlike conventional methods that primarily rely on textual representations and attention mechanisms, our approach enhances semantic understanding by integrating relevant external information. We introduce a fusion module to amalgamate the semantic insights derived from the text and the external knowledge. Our methodology’s efficacy is evidenced through comprehensive experiments conducted on diverse datasets, encompassing natural language inference, text classification, and medical natural language inference. The results unequivocally indicate a significant enhancement in model performance, underscoring the effectiveness of incorporating external knowledge into text-matching tasks.