Creolization and code-switching are closely related contact-induced linguistic phenomena, yet little attention has been paid to the connection between them. In this paper, we propose an agent-based cognitive model which provides a linkage between these two phenomena focusing on the statistical regularization of language use. That is, we identify that creolization as a conventionalization process and code-switching as flexible language choice can emerge from the same cognitive model in different social environments. Our model postulates a social structure of bilingual and monolingual populations, in which a set of agents seek for optimal communicative strategy shaped by multiple cognitive constraints. The simulation results show that our model successfully captures both phenomena as two ends of a continuum, characterized by varying degrees of regularization in the use of linguistic constructions from multiple source languages. The model also reveals a subtle dynamic between social structure and individual-level cognitive constraints.

The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.cluebenchmarks.com