A method Based on an Attention Mechanism to Measure the Similarity of two Sentences

Seyed Vahid Moravvej, Mehdi Joodaki, Mohammad Javad Maleki Kahaki, Moein Salimi Sartakhti
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引用次数: 18

Abstract

Bidirectional LSTMs and the attention mechanism play an essential role in many areas of natural language processing. Many studies give equal importance to words, which leads to a flawed model. This research offers a method based on Attention-Based Bidirectional Long-Short Term Memory (BLSTM) to solve the problem of plagiarism at the sentence level. For this purpose, word embedding is first made with Glove and Word2Vec methods and is considered as initial embedding. Then the two BLSTM networks are used separately for sentence embedding. Finally, the embedding of sentences and their differences are connected and passed through a classifier. We evaluate our model on two datasets of Persian and English. The evaluation results show the superiority of the proposed model over other compared methods.
基于注意机制的两句相似度测量方法
双向lstm和注意机制在自然语言处理的许多领域发挥着重要作用。许多研究将语言同等重要,这导致了一个有缺陷的模型。本研究提出了一种基于注意的双向长短期记忆(BLSTM)方法来解决句子层面的抄袭问题。为此,首先使用Glove和Word2Vec方法进行词嵌入,并将其视为初始嵌入。然后分别使用两个BLSTM网络进行句子嵌入。最后,将嵌入的句子及其差异连接起来,并通过分类器传递。我们在波斯语和英语两个数据集上评估了我们的模型。评价结果表明,该模型优于其他比较方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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