1. TABAS: Text augmentation based on attention score for text classification model
- Author
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Jong Woo Kim, So Young Jun, Seung Joo Yoon, and Yeong Jae Yu
- Subjects
Computer Networks and Communications ,Computer science ,business.industry ,Machine learning ,computer.software_genre ,Convolutional neural network ,Artificial Intelligence ,Hardware and Architecture ,Benchmark (computing) ,Artificial intelligence ,business ,computer ,Software ,Word (computer architecture) ,Information Systems - Abstract
To improve the performance of text classification, we propose text augmentation based on attention score (TABAS). We recognized that a criterion for selecting a replacement word rather than a random selection was necessary. Therefore, TABAS utilizes attention scores for text modification, processing only words with the same entity and part-of-speech tags to consider informational aspects. To verify this approach, we used two benchmark tasks. As a result, TABAS can significantly improve performance, both recurrent and convolutional neural networks. Furthermore, we confirm that it provides a practical way to develop deep-learning models by saving costs on making additional datasets.
- Published
- 2022
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