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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context
%A Lee, Hankyol
%A Yu, Youngjae
%A Kim, Gunhee
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lee-etal-2020-augmenting
%X 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.
%R 10.18653/v1/2020.figlang-1.2
%U https://aclanthology.org/2020.figlang-1.2
%U https://doi.org/10.18653/v1/2020.figlang-1.2
%P 12-17

Markdown (Informal)

[Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context](https://aclanthology.org/2020.figlang-1.2) (Lee et al., Fig-Lang 2020)

• Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context (Lee et al., Fig-Lang 2020)

ACL

• Hankyol Lee, Youngjae Yu, and Gunhee Kim. 2020. Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context. In Proceedings of the Second Workshop on Figurative Language Processing, pages 12–17, Online. Association for Computational Linguistics.