LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error
  Correction
Yixuan Wang Baoxin Wang Yijun Liu Dayong Wu Wanxiang Che
Over-correction is a critical problem in Chinese grammatical error correction
(CGEC) task. Recent work using model ensemble methods based on voting can
effectively mitigate over-correction and improve the precision of the GEC
system. However, these methods still require the output of several GEC systems
and inevitably lead to reduced error recall. In this light, we propose the
LM-Combiner, a rewriting model that can directly modify the over-correction of
GEC system outputs without a model ensemble. Specifically, we train the model
on an over-correction dataset constructed through the proposed K-fold cross
inference method, which allows it to directly generate filtered sentences by
combining the original and the over-corrected text. In the inference stage, we
directly take the original sentences and the output results of other systems as
input and then obtain the filtered sentences through LM-Combiner. Experiments
on the FCGEC dataset show that our proposed method effectively alleviates the
over-correction of the original system (+18.2 Precision) while ensuring the
error recall remains unchanged. Besides, we find that LM-Combiner still has a
good rewriting performance even with small parameters and few training data,
and thus can cost-effectively mitigate the over-correction of black-box GEC
systems (e.g., ChatGPT).