Recognizing textual entailment aims to infer the semantic relationship between two sentences, usually divided into threecategories of entailment, contradiction and neutral. At present, most recognizing textual entailment methods use mutual attention todetermine the semantic relationship between sentences. This method can?only capture local interaction information and weaken the globalinteraction information. Accordingly,a recognizing textual entailment model that integrates syntactic structure and abstract information isproposed. By combining self-attention and mutual attention mechanisms,the global and local interactive information of sentences?
is considered,and the syntactic structure information of sentences is integrated, so as to accurately infer the semantic relationship betweensentences. We collect and sort out some multiple-choice questions of civil servant test questions,and then apply the model and the ideaof textual entailment recognition to these test questions.?
In the test questions,the method of abstract information extraction is used tosolve asymmetric length problems caused by long questions and short answers in civil service examination questions. Experimental resultsshow that the proposed model outperforms several benchmark models on public datasets and civil servant test questions.