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).

通过提出的 LM-Combiner 模型，直接修正语法错误纠正系统输出的过度修正，从而有效减轻原系统的过度修正问题，同时保持错误召回率不变。

LM-Combiner: 一个用于中文语法错误纠正的上下文重写模型

LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error  Correction

Large-scale language models (LLMs) has shown remarkable capability in various
of Natural Language Processing (NLP) tasks and attracted lots of attention
recently. However, some studies indicated that large language models fail to
achieve promising result beyond the state-of-the-art models in English
grammatical error correction (GEC) tasks. In this report, we aim to explore the
how large language models perform on Chinese grammatical error correction tasks
and provide guidance for future work. We conduct experiments with 3 different
LLMs of different model scale on 4 Chinese GEC dataset. Our experimental
results indicate that the performances of LLMs on automatic evaluation metrics
falls short of the previous sota models because of the problem of
over-correction. Furthermore, we also discover notable variations in the
performance of LLMs when evaluated on different data distributions. Our
findings demonstrates that further investigation is required for the
application of LLMs on Chinese GEC task.

大规模语言模型在中文语法错误纠正任务中的表现及其问题的调查报告。研究发现大语言模型在自动评估指标方面表现不如之前的模型，并存在过度纠正的问题。此外，不同数据分布下大语言模型的表现也存在明显差异。这些发现表明需要进一步研究大语言模型在中文语法错误纠正任务上的应用。

评估大型语言模型在中文语法错误修正任务中的能力

Evaluating the Capability of Large-scale Language Models on Chinese  Grammatical Error Correction Task

Chinese Text Error Correction (CTEC) aims to detect and correct errors in the
input text, which benefits human's daily life and various downstream tasks.
Recent approaches mainly employ Pre-trained Language Models (PLMs) to resolve
CTEC task and achieve tremendous success. However, previous approaches suffer
from issues of over-correction and under-correction, and the former is
especially conspicuous in the precision-critical CTEC task. To mitigate the
issue of overcorrection, we propose a novel model-agnostic progressive
multitask learning framework for CTEC, named ProTEC, which guides a CTEC model
to learn the task from easy to difficult. We divide CTEC task into three
sub-tasks from easy to difficult: Error Detection, Error Type Identification,
and Correction Result Generation. During the training process, ProTEC guides
the model to learn text error correction progressively by incorporating these
sub-tasks into a multi-task training objective. During the inference process,
the model completes these sub-tasks in turn to generate the correction results.
Extensive experiments and detailed analyses fully demonstrate the effectiveness
and efficiency of our proposed framework.

本文提出一种基于多任务学习的 CTEC 框架 ProTEC 来解决预训练语言模型在 CTEC 中产生的过度或欠度修改问题。该框架将 CTEC 任务分为三个子任务：错误检测、错误类型识别和更正结果生成，并通过多任务学习逐步引导模型从易到难地学习，有效地提高了 CTEC 任务的效果和效率。