# CCAE: A Corpus of Chinese-based Asian Englishes

arXiv: https://arxiv.org/abs/2310.05381 · HTML: https://mlsanity.com/paper/2310.05381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 15 of 15)

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- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
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- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [A Systematic Analysis of Morphological Content in BERT Models for Multiple Languages](https://mlsanity.com/paper/2004.03032/) (2020)
- [Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations](https://mlsanity.com/paper/1910.05479/) (2019)

## Abstract (arXiv)

Language models have been foundations in various scenarios of NLP applications, but it has not been well applied in language variety studies, even for the most popular language like English. This paper represents one of the few initial efforts to utilize the NLP technology in the paradigm of World Englishes, specifically in creating a multi-variety corpus for studying Asian Englishes. We present an overview of the CCAE -- Corpus of Chinese-based Asian English, a suite of corpora comprising six Chinese-based Asian English varieties. It is based on 340 million tokens in 448 thousand web documents from six regions. The ontology of data would make the corpus a helpful resource with enormous research potential for Asian Englishes (especially for Chinese Englishes for which there has not been a publicly accessible corpus yet so far) and an ideal source for variety-specific language modeling and downstream tasks, thus setting the stage for NLP-based World Englishes studies. And preliminary experiments on this corpus reveal the practical value of CCAE. Finally, we make CCAE available at hrefhttps://huggingface.co/datasets/CCAE/CCAE-Corpusthis https URL.