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저자 김호승, 강용훈, 이지형 
학회명 The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation 
학회명 (약자) LREC-COLING 2024 
pp.  
학회시작일 2024-05-20 
학회종료일 2024-05-25 
비고  

Title : STAGE: Simple Text Data Augmentation by Graph Exploration


Abstact : Pre-trained language models (PLMs) are widely used for various tasks, but fine-tuning them requires sufficient data. Data augmentation approaches have been proposed as alternatives, but they vary in complexity, cost, and performance. To address these challenges, we propose STAGE (Simple Text Data Augmentation by Graph Exploration), a highly effective method for data augmentation. STAGE utilizes simple modification operations such as insertions, deletions, replacements, and swaps. However, what distinguishes STAGE lies in the selection of optimal words for each modification. This is achieved by leveraging a word-relation graph called the co-graph. The co-graph takes into account both word frequency and co-occurrence, providing valuable information for operand selection. To assess the performance of STAGE, we conduct evaluations using seven representative datasets and three different PLMs. Our results demonstrate the effectiveness of STAGE across diverse data domains, varying data sizes, and different PLMs. Also, STAGE demonstrates superior performance when compared to previous methods that use simple modification operations or large language models like GPT3.

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