期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：8
DOI：10.14569/IJACSA.2021.0120891
语种：English
出版社：Science and Information Society (SAI)
摘要：The paraphrase identification task identifies whether two text segments share the same meaning, thereby playing a crucial role in various applications, such as computer-assisted translation, question answering, machine translation, etc. Although the literature on paraphrase identification in English and other popular languages is vast and growing, the research on this topic in Vietnamese remains relatively untapped. In this paper, we propose a novel method to classify Vietnamese sentence paraphrases, which deploys both the pre-trained model to exploit the semantic context and linguistic knowledge to provide further information in the identification process. Two branches of neural networks built in the Siamese architecture are also responsible for learning the differences among the sentence representations. To evaluate the proposed method, we present experiments on two existing Vietnamese sentence paraphrase corpora. The results show that for the same corpora, our method using the PhoBERT as a feature vector yields 94.97% F1-score on the VnPara corpus and 93.49% F1-score on the VNPC corpus. They are better than the results of the Siamese LSTM method and the pre-trained models.
