用心理学机制提升翻译幽默感，让机器更懂笑话
Psychology-Driven Enhancement of Humour Translation
- 模仿人类思维链分解笑话结构，增强可读性
- 结合幽默理论，翻译后幽默度提升7.75%，流畅度+2.81%
- 适合需要精准传达笑点的跨文化内容创作
幽默翻译在跨文化交流中起着关键作用。尽管现有大语言模型具备通用翻译能力，但在幽默翻译上仍存在语言干扰和译文缺乏幽默的问题。本文提出一种受心理学启发的幽默分解机制（HDM），利用思维链（CoT）模拟人类思考过程，促使大模型优化幽默文本的可读性，并在机制中融入幽默理论以增强译文中的幽默元素。在开源幽默数据集上的自动评估实验表明，该方法显著提升了幽默翻译质量，生成文本在幽默度上平均提升7.75%，流畅度提升2.81%，连贯性提升6.13%。
原文摘要 · Abstract (English)
Humour translation plays a vital role as a bridge between different cultures, fostering understanding and communication. Although most existing Large Language Models (LLMs) are capable of general translation tasks, these models still struggle with humour translation, which is especially reflected through linguistic interference and lacking humour in translated text. In this paper, we propose a psychology-inspired Humour Decomposition Mechanism (HDM) that utilises Chain-of-Thought (CoT) to imitate the ability of the human thought process, stimulating LLMs to optimise the readability of translated humorous texts. Moreover, we integrate humour theory in HDM to further enhance the humorous elements in the translated text. Our automatic evaluation experiments on open-source humour datasets demonstrate that our method significantly improves the quality of humour translation, yielding average gains of 7.75\% in humour, 2.81\% in fluency, and 6.13\% in coherence of the generated text.
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