Abstract
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.
Abstract (translated)
本文解决了在专业领域中准确翻译技术术语的挑战，这些术语对于清晰沟通至关重要。我们引入了Parenthetical Terminology Translation（PTT）任务，旨在通过在原文术语旁边显示其翻译来缓解潜在的不准确性。为了实现这种方法，我们使用合作方法生成了一个PTT数据集，并使用知识蒸馏对传统的神经机器翻译（NMT）模型和小型语言模型（sLMs）进行微调。此外，我们还开发了一种新的评估指标，以评估总翻译准确性以及术语的正确父式呈现。我们的研究结果表明，sLMs并没有始终优于NMT模型，而微调证明比少量的提示更有效，特别是在目标语言中持续预训练的模型中。这些见解有助于推动更可靠术语翻译方法的发展。
URL
https://arxiv.org/abs/2410.00683
