Dynamic decoding and dual synthetic data for automatic correction of grammar in low-resource scenario

Author:

Musyafa Ahmad12,Gao Ying1,Solyman Aiman3,Khan Siraj4,Cai Wentian1,Khan Muhammad Faizan1

Affiliation:

1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

2. Department of Informatics Engineering, Pamulang University, South Tangerang, Indonesia

3. Department of Computer Science, University of Milan, Milan, Italy

4. School of Software Engineering, South China University of Technology, Guangzhou, China

Abstract

Grammar error correction systems are pivotal in the field of natural language processing (NLP), with a primary focus on identifying and correcting the grammatical integrity of written text. This is crucial for both language learning and formal communication. Recently, neural machine translation (NMT) has emerged as a promising approach in high demand. However, this approach faces significant challenges, particularly the scarcity of training data and the complexity of grammar error correction (GEC), especially for low-resource languages such as Indonesian. To address these challenges, we propose InSpelPoS, a confusion method that combines two synthetic data generation methods: the Inverted Spellchecker and Patterns+POS. Furthermore, we introduce an adapted seq2seq framework equipped with a dynamic decoding method and state-of-the-art Transformer-based neural language models to enhance the accuracy and efficiency of GEC. The dynamic decoding method is capable of navigating the complexities of GEC and correcting a wide range of errors, including contextual and grammatical errors. The proposed model leverages the contextual information of words and sentences to generate a corrected output. To assess the effectiveness of our proposed framework, we conducted experiments using synthetic data and compared its performance with existing GEC systems. The results demonstrate a significant improvement in the accuracy of Indonesian GEC compared to existing methods.

Funder

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

Publisher

PeerJ

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