Authors : Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, Naama Zwerdling IBM Research AI, University of Haifa, Israel, Technion - Israel Institute of Technology
Paper : https://aaai.org/Papers/AAAI/2020GB/AAAI-AnabyA.4027.pdf Code : None AAAI 2020 Poster session
GPT-2 를 이용하여 데이터셋을 생성 한 후, 원래 데이터로 학습 된 classifier를 통해 생성된 데이터를 필터링 하여 남은 것들을 원래 데이터에 추가하는 방식으로 Data Augmentation.
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Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a powerful pre-trained neural network model to artificially synthesize new labeled data for supervised learning. We mainly focus on cases with scarce labeled data. Our method, referred to as language-model-based data augmentation (LAMBADA), involves fine-tuning a state-of-the-art language generator to a specific task through an initial training phase on the existing (usually small) labeled data. Using the fine-tuned model and given a class label, new sentences for the class are generated. Our process then filters these new sentences by using a classifier trained on the original data. In a series of experiments, we show that LAMBADA improves classifiers’ performance on a variety of datasets. Moreover, LAMBADA significantly improves upon the state-of-the-art techniques for data augmentation, specifically those applicable to text classification tasks with little data.