Deexaggeration
Abstract
We introduce a new task in hyperbole processing, deexaggeration, which concerns the recovery of the meaning of what is being exaggerated in a hyperbolic sentence in the form of a structured representation. In this paper, we lay the groundwork for the computational study of understanding hyperbole by (1) defining a structured representation to encode what is being exaggerated in a hyperbole in a non-hyperbolic manner, (2) annotating the hyperbolic sentences in two existing datasets, HYPO and HYPO-cn, using this structured representation, (3) conducting an empirical analysis of our annotated corpora, and (4) presenting preliminary results on the deexaggeration task.
Cite
Text
Kong et al. "Deexaggeration." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/581Markdown
[Kong et al. "Deexaggeration." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/kong2022ijcai-deexaggeration/) doi:10.24963/IJCAI.2022/581BibTeX
@inproceedings{kong2022ijcai-deexaggeration,
title = {{Deexaggeration}},
author = {Kong, Li and Li, Chuanyi and Ng, Vincent},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {4185-4192},
doi = {10.24963/IJCAI.2022/581},
url = {https://mlanthology.org/ijcai/2022/kong2022ijcai-deexaggeration/}
}