用知识蒸馏提升术语翻译准确率，括号保留原词更可靠
Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation
- 用大模型协作生成数据，通过知识蒸馏微调翻译模型
- 小模型经微调后表现优于零样本提示，尤其在目标语言持续预训练时
- 提出新评估指标，兼顾翻译准确性和括号格式正确性
本文针对专业领域中技术术语翻译不准确的问题，提出括号术语翻译（PTT）任务，即在翻译结果中以括号形式保留原文术语，以减少歧义。为此，我们采用大语言模型协作方式构建代表性PTT数据集，并运用知识蒸馏方法对传统神经机器翻译（NMT）模型及小型大语言模型（sLMs）进行微调。同时，设计了一种新型评估指标，用于衡量整体翻译准确率与术语括号呈现的正确性。实验表明，sLMs并未始终优于NMT模型，且微调效果优于少样本提示，尤其在模型经过目标语言持续预训练的情况下表现更佳。研究为提升术语翻译可靠性提供了有效方法。
原文摘要 · Abstract (English)
This paper addresses the challenge of accurately translating technical terms, which are crucial for clear communication in specialized fields. We introduce the Parenthetical Terminology Translation (PTT) task, designed to mitigate potential inaccuracies by displaying the original term in parentheses alongside its translation. To implement this approach, we generated a representative PTT dataset using a collaborative approach with large language models and applied knowledge distillation to fine-tune traditional Neural Machine Translation (NMT) models and small-sized Large Language Models (sLMs). Additionally, we developed a novel evaluation metric to assess both overall translation accuracy and the correct parenthetical presentation of terms. Our findings indicate that sLMs did not consistently outperform NMT models, with fine-tuning proving more effective than few-shot prompting, particularly in models with continued pre-training in the target language. These insights contribute to the advancement of more reliable terminology translation methodologies.
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