---
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
- text-generation
language:
- en
pretty_name: Colorful Candies Are Exciting
size_categories:
- 100K<n<1M
---
# CCAE: A Corpus of Chinese-based Asian Englishes

## Dataset Description

- **Repository:** https://github.com/jacklanda/CCAE
- **Paper:** 

### Dataset Summary

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 varietyspecific 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.

### Languages

Six varieties in asian areas of English: CHE, HKE, MCE, TWE, MYE, SGE

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

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### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

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### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

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### Other Known Limitations

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## Additional Information

### Dataset Curators

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### Licensing Information

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### Citation Information

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### Contributions

[More Information Needed]