I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection

TL;DR

This paper introduces I-WAS, a data augmentation technique using GPT-2 for simile detection, enhancing the diversity and quality of training data to improve NLP applications in literature.

Contribution

The paper presents a novel GPT-2 based data augmentation method specifically designed for simile detection, addressing data scarcity and diversity issues.

Findings

Improved simile detection accuracy with augmented data

Enhanced diversity of simile forms in the dataset

Effective augmentation method validated on a new diverse corpus

Abstract

Simile detection is a valuable task for many natural language processing (NLP)-based applications, particularly in the field of literature. However, existing research on simile detection often relies on corpora that are limited in size and do not adequately represent the full range of simile forms. To address this issue, we propose a simile data augmentation method based on \textbf{W}ord replacement And Sentence completion using the GPT-2 language model. Our iterative process called I-WAS, is designed to improve the quality of the augmented sentences. To better evaluate the performance of our method in real-world applications, we have compiled a corpus containing a more diverse set of simile forms for experimentation. Our experimental results demonstrate the effectiveness of our proposed data augmentation method for simile detection.