Hyun-Kyu Jeon  Yun-Gyung Cheong

10.5626/JOK.2021.48.10.1132

paraphrase generation Classification Model model-based learning Loss Function

Paraphrase generation is a task in which the model generates an output sentence conveying the same meaning as the given input text but with a different representation. Recently, paraphrase generation has been widely used for solving the task of using artificial neural networks with supervised learning between the model’s prediction and labels. However, this method gives limited information because it only detects the representational difference. For that reason, we propose a method to extract semantic information with classification models and use them for the training loss function. Our evaluations showed that the proposed method outperformed baseline models.