# YACLC: A Chinese Learner Corpus with Multidimensional Annotation

arXiv: https://arxiv.org/abs/2112.15043 · HTML: https://mlsanity.com/paper/2112.15043/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction](https://mlsanity.com/paper/2105.04443/) (2021)
- [The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction](https://mlsanity.com/paper/1907.08889/) (2019)
- [Controllable Data Synthesis Method for Grammatical Error Correction](https://mlsanity.com/paper/1909.13302/) (2019)

## Built on by (most cited first, 8 of 8)

- [On the (In)Effectiveness of Large Language Models for Chinese Text Correction](https://mlsanity.com/paper/2307.09007/) (2023)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.10442/) (2022)
- [FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.12364/) (2022)
- [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://mlsanity.com/paper/2305.16023/) (2023)
- [ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models](https://mlsanity.com/paper/2308.14353/) (2023)
- [Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts](https://mlsanity.com/paper/2408.09688/) (2024)
- [EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correction](https://mlsanity.com/paper/2407.00924/) (2024)

## Abstract (arXiv)

Learner corpus collects language data produced by L2 learners, that is second or foreign-language learners. This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction. However, there is little focus on learner corpus for Chinese as Foreign Language (CFL) learners. Therefore, we propose to construct a large-scale, multidimensional annotated Chinese learner corpus. To construct the corpus, we first obtain a large number of topic-rich texts generated by CFL learners. Then we design an annotation scheme including a sentence acceptability score as well as grammatical error and fluency-based corrections. We build a crowdsourcing platform to perform the annotation effectively (https://yaclc.wenmind.net). We name the corpus YACLC (Yet Another Chinese Learner Corpus) and release it as part of the CUGE benchmark (http://cuge.baai.ac.cn). By analyzing the original sentences and annotations in the corpus, we found that YACLC has a considerable size and very high annotation quality. We hope this corpus can further enhance the studies on Chinese International Education and Chinese automatic grammatical error correction.