Simile Identification with Pseudo Data Acquisition and Re-labeling
Abstract
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. We utilize mBART as the machine translation system. 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 (machine translation with rules). In addition, re-labeling with machine learning for the original pseudo data contributed to the improvement of the simile identification accuracy.
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