Research on the Translation of Chinese Complex Sentences With Large Language Model ChatGPT-4o —Taking English Translation of Chinese Flowing Sentences as an Example

Large language models (LLMs) have demonstrated outstanding performance in the field of machine translation. Many scholars have explored LLM-based translation from various perspectives, primarily focusing on the translation of routine, simple, and everyday texts. However, when it comes to the examina...

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Published inTheory and practice in language studies Vol. 15; no. 5; pp. 1541 - 1549
Main Author Dou, Ruifang
Format Journal Article
LanguageEnglish
Published London Academy Publication Co., LTD 01.05.2025
Academy Publication Co., Ltd
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ISSN1799-2591
2053-0692
DOI10.17507/tpls.1505.19

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Abstract Large language models (LLMs) have demonstrated outstanding performance in the field of machine translation. Many scholars have explored LLM-based translation from various perspectives, primarily focusing on the translation of routine, simple, and everyday texts. However, when it comes to the examination of specific sentence structures, especially complex sentences, the research is still somewhat insufficient. Therefore, this study attempts to investigate the translation of Chinese complex sentences by ChatGPT-4o, focusing on the translation of Chinese flowing sentences, a special sentence pattern in Chinese. The study examines how ChatGPT-4o handles the subject identification and translation of zero-subject sentence segments within Chinese flowing sentences, as well as the translation of the frozen sentence segments in Chinese flowing sentences. The research results indicate that: for the ten categories of subject identification in zero-subject sentence segments, ChatGPT-4o can generally identify them all, but occasionally makes ambiguous identification; ChatGPT-4o employs two translation strategies—one is to add subjects (mostly in the form of pronouns) to translate the zero-subject sentence segments into independent clauses, the other is to translate the zero-subject sentence segments into attributive structures; the accuracy of ChatGPT-4o’s translation of frozen sentence segments is inconsistent; ChatGPT-4o may adopt the literal meaning or metaphorical meaning of the frozen sentence segment, determining the form of the frozen sentence segment based on the overall structure of the flowing sentence.
AbstractList Large language models (LLMs) have demonstrated outstanding performance in the field of machine translation. Many scholars have explored LLM-based translation from various perspectives, primarily focusing on the translation of routine, simple, and everyday texts. However, when it comes to the examination of specific sentence structures, especially complex sentences, the research is still somewhat insufficient. Therefore, this study attempts to investigate the translation of Chinese complex sentences by ChatGPT-4o, focusing on the translation of Chinese flowing sentences, a special sentence pattern in Chinese. The study examines how ChatGPT-4o handles the subject identification and translation of zero-subject sentence segments within Chinese flowing sentences, as well as the translation of the frozen sentence segments in Chinese flowing sentences. The research results indicate that: for the ten categories of subject identification in zero-subject sentence segments, ChatGPT-4o can generally identify them all, but occasionally makes ambiguous identification; ChatGPT-4o employs two translation strategies-one is to add subjects (mostly in the form of pronouns) to translate the zero-subject sentence segments into independent clauses, the other is to translate the zero-subject sentence segments into attributive structures; the accuracy of ChatGPT-4o's translation of frozen sentence segments is inconsistent; ChatGPT-4o may adopt the literal meaning or metaphorical meaning of the frozen sentence segment, determining the form of the frozen sentence segment based on the overall structure of the flowing sentence.
Large language models (LLMs) have demonstrated outstanding performance in the field of machine translation. Many scholars have explored LLM-based translation from various perspectives, primarily focusing on the translation of routine, simple, and everyday texts. However, when it comes to the examination of specific sentence structures, especially complex sentences, the research is still somewhat insufficient. Therefore, this study attempts to investigate the translation of Chinese complex sentences by ChatGPT-4o, focusing on the translation of Chinese flowing sentences, a special sentence pattern in Chinese. The study examines how ChatGPT-4o handles the subject identification and translation of zero-subject sentence segments within Chinese flowing sentences, as well as the translation of the frozen sentence segments in Chinese flowing sentences. The research results indicate that: for the ten categories of subject identification in zero-subject sentence segments, ChatGPT-4o can generally identify them all, but occasionally makes ambiguous identification; ChatGPT-4o employs two translation strategies--one is to add subjects (mostly in the form of pronouns) to translate the zero-subject sentence segments into independent clauses, the other is to translate the zero-subject sentence segments into attributive structures; the accuracy of ChatGPT-4o's translation of frozen sentence segments is inconsistent; ChatGPT-4o may adopt the literal meaning or metaphorical meaning of the frozen sentence segment, determining the form of the frozen sentence segment based on the overall structure of the flowing sentence. Index Terms--LLMs, ChatGPT-4o, Chinese flowing sentences, translation
Audience Academic
Author Dou, Ruifang
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Snippet Large language models (LLMs) have demonstrated outstanding performance in the field of machine translation. Many scholars have explored LLM-based translation...
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StartPage 1541
SubjectTerms Accuracy
Algorithms
Chatbots
Chinese languages
Complexity
Creativity
Cultural Background
Cultural heritage
English
Grammatical subject
Human-computer interaction
Identification
Language
Language modeling
Language Processing
Large language models
Literature Reviews
Machine translation
Semantics
Sentence structure
Translating and interpreting
Translation
Translation methods and strategies
Title Research on the Translation of Chinese Complex Sentences With Large Language Model ChatGPT-4o —Taking English Translation of Chinese Flowing Sentences as an Example
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