Judgment Answer Inference Bsaed on Pre-trained Semantic Encoding
Currently,large-scale text question answering relies on sentence representation to retrieve answers from candidate texts,but it ig-nores that some answers require further reasoning and cannot be obtained directly from the text,such as judgment sentences.To solve such problems,a judgment sentence answer generation method for large-scale text is proposed.Firstly,in the semantic encoder,the semantic en-coder is obtained by continuing to pre-train large-scale texts,and the questions and cues are semantically encoded.Sceondly,in the answer generator module,positive and negative samples are constructed based on contrastive learning for data enhancement.Then fast characteriza-tion and matching of questions and large-scale text is achieved by using Faiss in the answer basis obtainer.The accuracy of the final judgment sentence question and answer is as high as 96.58%,which verifies the effectiveness of this method.
国家科技期刊平台
NSTL
万方数据
维普
