@inproceedings{lee-etal-2020-augmenting,
title = "Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context",
author = "Lee, Hankyol and
Yu, Youngjae and
Kim, Gunhee",
editor = "Klebanov, Beata Beigman and
Shutova, Ekaterina and
Lichtenstein, Patricia and
Muresan, Smaranda and
Wee, Chee and
Feldman, Anna and
Ghosh, Debanjan",
booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.figlang-1.2",
doi = "10.18653/v1/2020.figlang-1.2",
pages = "12--17",
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.",
}
Markdown (Informal)
[Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context](https://aclanthology.org/2020.figlang-1.2) (Lee et al., Fig-Lang 2020)
ACL