  We present a novel data augmentation technique, CRA (Contextual Response
Augmentation), which utilizes conversational context to generate meaningful
samples for training. We also mitigate the issues regarding unbalanced context
lengths by changing the input-output format of the model such that it can deal
with varying context lengths effectively. Specifically, our proposed model,
trained with the proposed data augmentation technique, participated in the
sarcasm detection task of FigLang2020, have won and achieves the best
performance in both Reddit and Twitter datasets.
