Leveraging artificial intelligence through long short-term memory approach for correcting faults in Chinese language sentences

Abstract

This research focus on leveraging artificial intelligence (AI) to manage the challenges faced by non-native speakers in correcting faults and misconstructions in Chinese language sentences. Learners commonly struggle with mispronunciation, incorrect character usage, improper sentence structures, and grammatical mistakes. To tackle these issues, this study generally aims to improve and optimize AI for correcting faults in Chinese language for non-native speakers. This project employs long short-term memory (LSTM) approach based on Hanyu Shuiping Kaoshi (HSK) word ordering errors (WOE) dataset. The effectiveness of leveraging LSTM in detecting and correcting errors in Chinese language sentence have been demonstrated. LSTM shows the capability to be learn Chinese sentence structure, identify mistakes, and correct them. In summary, this research seeks to benefits the power of AI to provide innovative solutions for detecting, correcting faults and misconstructions in Chinese language sentences. This paper essentially useful for those who wish to learn how to correct their Chinese writing and enhance language proficiency among non-native speakers.