Blow the Dog Whistle: A C hinese Dataset for Cant Understanding with Common Sense and World Knowledge (2021)

Abstract: 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.

## introduction a cant 2 (also known as doublespeak, cryptolect, argot, anti-language or secret language) is the jargon or language of a group, often employed to exclude or mislead people outside the group @xcite . cant is crucial for understanding advertising @xcite and both ancient and modern comedy @xcite . also, it is the cornerstone for infamous dogwhistle politics @xcite . here, we summarize the key elements for cant: (1) both a cant and its reference (i.e., hidden word) should be in the form of common natural text (not another symbol system, e.g., morse code). ( 2 ) there is some shared information between the cant users (i.e., the insiders) that is not provided to the people outside the group. (3) a cant should be deceptive and remain undetected to avoid being decrypted by people outside the group (i.e., the outsiders). these elements make the creation and understanding of cant subtle and hard to observe @xcite . to the best of our knowledge, currently there are very few resources available for the research of cant. in this paper, we create a dataset for studying cant, dogwhistle, centered around the aforementioned key elements (examples shown in figure 1 ). we collect the data with a well-designed online game under a player-versus-player setting (see section 3.1). the dataset includes abundant and diverse cant for a wide spectrum of hidden words. we find that cant understanding requires a deep understanding of language, common sense and world knowledge, making it a good testbed for next-generation pretrained language models. our dataset also serves as a timely and complex language resource that can help models perform better on other tasks through intermediate task transfer @xcite . ## related work the use of cant has long been studied in linguistics research @xcite @xcite @xcite @xcite . however, due to a lack of language resources, there are few studies in computational linguistics research. @xcite attempted to model the dogwhistle communications with a functional, agentbased method. as a related topic in computational linguistics, some previous studies investigate coded names in human language. @xcite analyzed and generated coded names of public figures. @xcite designed an automatic system to decode the coded names. @xcite exploited a knowledge graph to identify coded names. @xcite leveraged multi-modal information to align coded names with their references. our work differs from the above in the following ways: (1) previous studies focused on coded names for public figures; the source and variety of these coded names is limited. the hidden words in our dataset are sampled from a common dictionary and are of high diversity. (2) the coded names in previous studies are used by users to bypass a censor (mostly a rule-based automatic text matching system). conversely, our data are collected under an adversarial setting, pressuring users to mislead human adversaries. thus, our work is ideal for evaluating recent progress on natural language understanding (nlu) @xcite @xcite @xcite @xcite . ## data collection previous studies @xcite reveal that gamification can often improve the quality of collected data. instead of collecting data from the wild like most datasets @xcite @xcite , we collect the data from historical game records of decrypto online, a well-designed online board game. the screenshot of the user interface is shown in figure 2 . ## conclusion and future work in this paper, we propose dogwhistle, a new chinese dataset for cant creation, understanding and decryption. we evaluate word embeddings and pretrained language models on the dataset. the gap between human performance and model results indicates that our dataset is challenging and promising for evaluating new pretrained language models. for future work, we plan to leverage this dataset to train agents to compete against each other, to better understand verbal intelligence and teach agents to reason, guess and deceive in the form of natural language to make new progress at higher levels of world scope @xcite .