Results 271 to 280 of about 284,138 (314)

A Comparison of GEC Tools for Grammatical Error Correction in English

2025 MIPRO 48th ICT and Electronics Convention
Using the Building Educational Application (BEA) benchmark11https://codalab.lisn.upsaclay.fr/competitions/4057, this study compares the capabilities of Google Gemini22https://gemini.google.com/, ChatGPT33https://openai.com/chatgpt, DeepSeek44https://www ...
J. Virtanen, M. Toshevska
semanticscholar   +1 more source

Data Augmentation for Spoken Grammatical Error Correction

Slate
While there exist strong benchmark datasets for grammatical error correction (GEC), high-quality annotated spoken datasets for Spoken GEC (SGEC) are still under-resourced.
Penny Karanasou, Mengjie Qian, Stefano Bannò, Mark Gales, Kate Knill   +4 more
semanticscholar   +1 more source

Targeted Syntactic Evaluation for Grammatical Error Correction

Annual Meeting of the Association for Computational Linguistics
Language learners encounter a wide range of grammar items across the beginner, intermediate, and advanced levels. To develop grammatical error correction (GEC) models effectively, it is crucial to identify which grammar items are easier or more ...
Aomi Koyama, Masato Mita, Su-Youn Yoon, Y. Takama, Mamoru Komachi   +4 more
semanticscholar   +1 more source

RE2: improving Chinese grammatical error correction via retrieving appropriate examples with explanation

Frontiers of Computer Science
The primary objective of Chinese grammatical error correction (CGEC) is to detect and correct errors in Chinese sentences. Recent research shows that large language models (LLMs) have been applied to CGEC with significant results.
Baoxin Wang, Yumeng Luo, Yixuan Wang, Dayong Wu, Wanxiang Che, Shijin Wang   +5 more
semanticscholar   +1 more source

Prompting open-source and commercial language models for grammatical error correction of English learner text

Annual Meeting of the Association for Computational Linguistics
Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown that we can elicit attempts at grammatical error correction (GEC) from LLMs ...
Christopher Davis, Andrew Caines, Oistein Andersen, Shiva Taslimipoor, H. Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, P. Buttery   +8 more
semanticscholar   +1 more source

No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models

Conference of the European Chapter of the Association for Computational Linguistics
Grammatical Error Correction (GEC) enhances language proficiency and promotes effective communication, but research has primarily centered around English.
Agnes Luhtaru, Elizaveta Korotkova, Mark Fishel   +2 more
semanticscholar   +1 more source

Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency

International Conference on Language Resources and Evaluation
This paper proposes an analysis of prompting strategies for grammatical error correction (GEC) with selected large language models (LLM) based on language proficiency.
Min Zeng, Jiexin Kuang, Mengyang Qiu, Jayoung Song, Jungyeul Park   +4 more
semanticscholar   +1 more source

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

Annual Meeting of the Association for Computational Linguistics
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 ...
Yinghui Li, S. Qin, Jingheng Ye, Shirong Ma, Yangning Li, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu   +9 more
semanticscholar   +1 more source

Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models

Workshop on Innovative Use of NLP for Building Educational Applications
In this paper, we carry out experimental research on Grammatical Error Correction, delving into the nuances of single-model systems, comparing the efficiency of ensembling and ranking methods, and exploring the application of large language models to GEC
Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi, Artem Chernodub, Oleksandr Korniienko, I. Samokhin   +5 more
semanticscholar   +1 more source

Improving Grammatical Error Correction via Contextual Data Augmentation

Annual Meeting of the Association for Computational Linguistics
Nowadays, data augmentation through synthetic data has been widely used in the field of Grammatical Error Correction (GEC) to alleviate the problem of data scarcity.
Yixuan Wang, Baoxin Wang, Yijun Liu, Qingfu Zhu, Dayong Wu, Wanxiang Che   +5 more
semanticscholar   +1 more source