Research on Recognition Textual Entailment with Integrated Attention Mechanism and Lexical Information

Dequan Zheng, Haochen Liu, Feng Yu

Abstract: Recognition Textual Entailment is a fundamental research direction in natural language processing. However, due to the complexity of sentence structure and the multiple parts of speech of words, it affects the model's deep understanding of sentences. From a linguistic perspective, different parts of speech have different understandings of sentences. We believe that a complete sentence consists of two parts: sentence structure and syntactic structure. In response to this issue, this article proposes a recognition textual entailment model that integrates attention mechanism and part of speech tagging. NLTK (Natural Language Toolkit) is used to annotate the part of speech of sentences, and the part of speech information is interactively fused with the sentence features extracted by the Conformer model. After part of speech tagging, the model can better grasp the syntactic structure of sentences. Experiments have shown that the above method combines sentence structure and syntactic structure, which can effectively enhance the model's understanding of deep semantics and improve its accuracy compared to classical models.