Abstract
Semantic similarity evaluation is used in various fields such as question-and-answering and plagiarism testing, and many studies have been conducted into this problem. In previous studies using neural networks to evaluate semantic similarity, similarity has been measured using global information of sentence pairs. However, since sentences do not only have one meaning but a variety of meanings, using only global information can have a negative effect on performance improvement. Therefore, in this study, we propose a model that uses global information and local information simultaneously to evaluate the semantic similarity of sentence pairs. The proposed model can adjust whether to focus more on global information or local information through a weight parameter. As a result of the experiment, the proposed model can show that the accuracy is higher than existing models that use only global information.
Highlights
Semantic similarity evaluation is used in various fields such as machine translation, information retrieval, question-and-answering, and plagiarism detection [1,2,3,4]
We propose a model that uses global and local features together for semantic similarity evaluation of sentence pairs
To evaluate the semantic similarity of sentence pairs, we propose a model that uses global features, entire sentence information, and local features, localized sentence information, simultaneously
Summary
Semantic similarity evaluation is used in various fields such as machine translation, information retrieval, question-and-answering, and plagiarism detection [1,2,3,4]. Semantic similarity is measured for two texts, regardless of the length, the location of the corresponding words, and their contexts. These semantic similarity evaluations cost a lot of time and money in order for a person to judge directly. To solve this problem, past studies have used bilingual evaluation understudy (BLEU) [5] or metric for evaluation of translation with explicit ordering (METEOR) [6]. Recent studies [2,3,4] have shown good performance using artificial neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM) and gated recurrent unit (GRU), to evaluate the semantic similarity of sentence pairs
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