{"title":"Paraphrase Identification Based on Interpretable Mechanism","authors":"Lin Li, BinBin Lai, Jiangping Huang","doi":"10.1109/AIAM54119.2021.00090","DOIUrl":null,"url":null,"abstract":"Paraphrase identification is an important branch of natural language understanding. This paper proposes a vector representation method integrating explanatory text by adding the semantic features of explanatory text to the word vector representation, the vector representation integrates the common interpretation information of words outside the sentence, so as to enrich the semantics of vector representation. This paper obtains explanatory texts from the corpus of Modern Chinese Dictionary. Using a variety of neural network structures on the LCQMC, this work obtained more than 1% performance improvement with fusing explanatory text. The experimental results show that the vector representation of fused explanatory text is more suitable for the task of paraphrase recognition.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIAM54119.2021.00090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Paraphrase identification is an important branch of natural language understanding. This paper proposes a vector representation method integrating explanatory text by adding the semantic features of explanatory text to the word vector representation, the vector representation integrates the common interpretation information of words outside the sentence, so as to enrich the semantics of vector representation. This paper obtains explanatory texts from the corpus of Modern Chinese Dictionary. Using a variety of neural network structures on the LCQMC, this work obtained more than 1% performance improvement with fusing explanatory text. The experimental results show that the vector representation of fused explanatory text is more suitable for the task of paraphrase recognition.