arXiv Open Access 2020

Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context

Hankyol Lee Youngjae Yu Gunhee Kim

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Abstrak

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.

Topik & Kata Kunci

cs.CL

Penulis (3)

Hankyol Lee

Youngjae Yu

Gunhee Kim

Format Sitasi

APA MLA BibTeX

Lee, H., Yu, Y., Kim, G. (2020). Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context. https://arxiv.org/abs/2006.06259

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓