Pseudo data acquisition using machine translation and simile identification

The simile is a kind of figurative language. It expresses the target of the figurative language by using some typical phrases such as "like". It is important to distinguish whether the sentence is a simile or a literal for understanding a sentence. However, a large amount of data is required to generate a classifier by machine learning. Moreover, creating the dataset is costly. In this paper, we propose a pseudo dataset acquisition method for simile identification. We first construct a dataset of simile and literal sentences using machine translation. This process automatically generates pseudo simile and literal instances from three types of corpora. Then, we apply some machine learning approaches to the simile identification task. We compare Support Vector Machine, Naive Bayes, and BERT in the experiment. The experimental result shows the validity of the pseudo dataset as compared with a simple baseline. For the fine-tuning of BERT, our large pseudo training data were more effective than a small manual training data.