Results 11 to 20 of about 1,814 (232)

Focus Is What You Need For Chinese Grammatical Error Correction

CoRR, 2022
Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text. In the long term, researchers regard CGEC as a task with a certain degree of uncertainty, that is, an ungrammatical sentence may often have multiple references.
Jingheng Ye, Yinghui Li, Shirong Ma, Rui Xie 0005, Wei Wu 0014, Hai-Tao Zheng 0002   +5 more
openaire   +3 more sources

Research progress on Chinese and English text error correction [PDF]

ITM Web of Conferences
Text error correction is an essential task in natural language processing (NLP) that focuses on automatically identifying and correcting errors in written text. With the increasing amount of digital text in both Chinese and English, errors such as typos,
Wang Yao
doaj   +2 more sources

From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction

Findings of the Association for Computational Linguistics: EMNLP 2022, 2022
Chinese Grammatical Error Correction (CGEC) aims to generate a correct sentence from an erroneous sequence, where different kinds of errors are mixed.This paper divides the CGEC task into two steps, namely spelling error correction and grammatical error correction.Specially, we propose a novel zero-shot approach for spelling error correction, which is ...
Xiuyu Wu, Yunfang Wu
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A Hybrid System for Chinese Grammatical Error Diagnosis and Correction [PDF]

Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, 2018
This paper introduces the DM_NLP team’s system for NLPTEA 2018 shared task of Chinese Grammatical Error Diagnosis (CGED), which can be used to detect and correct grammatical errors in texts written by Chinese as a Foreign Language (CFL) learners.
Chen Li, Junpei Zhou, Zuyi Bao, Hengyou Liu, Guangwei Xu, Linlin Li 0001   +5 more
openaire   +2 more sources

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

CoRR, 2023
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.
Qu, Fanyi, Tang, Chenming, Wu, Yunfang
openaire   +3 more sources

Exploring language learning and corrective feedback in an eTandem project [PDF]

Journal of China Computer-Assisted Language Learning, 2021
Many studies about eTandem and language learning stem from learners in Western institutions of higher education. Unfortunately, there is a lack of research investigating the telecollaboration regarding language development between learners in the East ...
Tang Jinlan, Qian Kan, Wang Na, Hu Xiaona   +3 more
doaj   +2 more sources

Heterogeneous Recycle Generation for Chinese Grammatical Error Correction

Proceedings of the 28th International Conference on Computational Linguistics, 2020
Most recent works in the field of grammatical error correction (GEC) rely on neural machine translation-based models. Although these models boast impressive performance, they require a massive amount of data to properly train. Furthermore, NMT-based systems treat GEC purely as a translation task and overlook the editing aspect of it.
Charles Hinson, Hen-Hsen Huang, Hsin-Hsi Chen   +2 more
openaire   +2 more sources

EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correction

Proceedings of the AAAI Conference on Artificial Intelligence
Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited scenario, where they ignore the interaction between corrections and explanations and have not established a corresponding comprehensive benchmark. To bridge the gap, this paper first introduces the task of EXplainable GEC (EXGEC), which focuses on the ...
Jingheng Ye, Shang Qin, Yinghui Li, Xuxin Cheng, Libo Qin 0001, Hai-Tao Zheng 0002, Ying Shen 0001, Peng Xing, Zishan Xu, Guo Cheng, Wenhao Jiang   +10 more
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Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

CoRR
Recently, Large Language Models (LLMs) have been widely studied by researchers for their roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese Grammatical Error Correction (CGEC) aims to correct all potential grammatical errors in the input sentences.
Yinghui Li, Shang Qin, Jingheng Ye, Shirong Ma, Yangning Li, Libo Qin 0001, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng 0002, Philip S. Yu   +9 more
core   +4 more sources

Revisiting the Evaluation for Chinese Grammatical Error Correction

Journal of Advanced Computational Intelligence and Intelligent Informatics
English grammar error correction (GEC) has been a popular topic over the past decade. The appropriateness of automatic evaluations, e.g., the combination of metrics and reference types, has been thoroughly studied for English GEC. Yet, such systematic investigations on the Chinese GEC are still insufficient.
Hongfei Wang, Zhousi Chen, Zizheng Zhang, Zhidong Ling, Xiaomeng Pan, Wenjie Duan, Masato Mita, Mamoru Komachi   +7 more
core   +3 more sources