NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from
  Native Speaker Texts
Yue Zhang Bo Zhang Haochen Jiang Zhenghua Li Chen Li Fei Huang Min Zhang
We introduce NaSGEC, a new dataset to facilitate research on Chinese
grammatical error correction (CGEC) for native speaker texts from multiple
domains. Previous CGEC research primarily focuses on correcting texts from a
single domain, especially learner essays. To broaden the target domain, we
annotate multiple references for 12,500 sentences from three native domains,
i.e., social media, scientific writing, and examination. We provide solid
benchmark results for NaSGEC by employing cutting-edge CGEC models and
different training data. We further perform detailed analyses of the
connections and gaps between our domains from both empirical and statistical
views. We hope this work can inspire future studies on an important but
under-explored direction--cross-domain GEC.