Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge
This work addresses a data gap for researchers in computational linguistics studying cant, which is important for applications like advertising and politics, but it is incremental as it primarily provides a new dataset.
The authors tackled the lack of datasets for computational research on cant by creating a large and diverse Chinese dataset, and they found that cant understanding requires deep language understanding, common sense, and world knowledge, making it a good testbed for pretrained language models.
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.