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 in | Theory and practice in language studies Vol. 15; no. 5; pp. 1541 - 1549 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
London
Academy Publication Co., LTD
01.05.2025
Academy Publication Co., Ltd |
Subjects | |
Online Access | Get full text |
ISSN | 1799-2591 2053-0692 |
DOI | 10.17507/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. |
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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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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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