Author
Listed:
- Cailing Xiang
(School of Foreign Languages, Northeast Forestry University, Harbin, Heilongjiang, 150040, China)
Abstract
ChatGPT, an advanced language model by OpenAI, enhances translation with its powerful language generation and understanding. In comparison to traditional human translation, ChatGPT is less costly, time-consuming, and knowledge-constraint, showcasing the substantial value of its application in translation practice. In the context of globalization, forestry translation plays an increasing role in facilitating global forestry development. To meet the growing need for efficient and high-quality translation in the forestry sector, this paper did research on the effectiveness of ChatGPT in the translation of forestry sci-tech texts. Combining quantitative analysis using BLEU and TER scores with qualitative evaluations by domain experts, this study compares the quality of translations produced by ChatGPT with that of the three mainstream machine translation tools in the market—Google Translate, Youdao Translation, and DeepL Translator regarding the translations’ accuracy and readability. The findings reveal that while ChatGPT excels in domain-specific terminology and context-sensitive meanings, it faces challenges in dealing with texts with special sentence structures and making the translations adaptable. By identifying the strengths and limitations of ChatGPT in translating forestry sci-tech texts, this research illustrates that there is great potential for ChatGPT’s application in forestry translation. Additionally, the study provides insights that can guide the development and refinement of machine translation systems to better meet the needs of specialized fields, ultimately facilitating more effective global communication and knowledge sharing.
Suggested Citation
Cailing Xiang, 2024.
"Study on the Effectiveness of ChatGPT in Translating Forestry Sci-tech Texts,"
International Journal of Linguistics, Literature and Translation, Al-Kindi Center for Research and Development, vol. 7(9), pages 88-94, August.
Handle:
RePEc:alk:ijllt0:v:7:y:2024:i:9:id:7635
DOI: 10.32996/ijllt.2024.7.9.11
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