  Cant is important for understanding advertising, comedies and dog-whistle
politics. However, computational research on cant is hindered by a lack of
available datasets. In this paper, we propose a large and diverse Chinese
dataset for creating and understanding cant from a computational linguistics
perspective. We formulate a task for cant understanding and provide both
quantitative and qualitative analysis for tested word embedding similarity and
pretrained language models. Experiments suggest that such a task requires deep
language understanding, common sense, and world knowledge and thus can be a
good testbed for pretrained language models and help models perform better on
other tasks. The code is available at https://github.com/JetRunner/dogwhistle.
The data and leaderboard are available at
https://competitions.codalab.org/competitions/30451.
