The typical workflow for a professional translator to translate a document from its source language (SL) to a target language (TL) is not always focused on what many language models in natural language processing (NLP) do - predict the next word in a series of words. While high-resource languages like English and French are reported to achieve near human parity using common metrics for measurement such as BLEU and COMET, we find that an important step is being missed: the translation of technical terms, specifically acronyms. Some state-of-the art machine translation systems like Google Translate which are publicly available can be erroneous when dealing with acronyms - as much as 50% in our findings. This article addresses acronym disambiguation for MT systems by proposing an additional step to the SL-TL (FR-EN) translation workflow where we first offer a new acronym corpus for public consumption and then experiment with a search-based thresholding algorithm that achieves nearly 10% increase when compared to Google Translate and OpusMT.

本研究针对机器翻译中技术术语（尤其是缩写）翻译缺失的问题，提出了在源语言（SL）到目标语言（TL）的翻译流程中加入新的步骤，通过共享新缩写语料库并实验搜索阈值算法来改善翻译效果。研究发现，该方法比谷歌翻译和OpusMT提高了近10%的准确率，显示出对提升机器翻译质量的潜在影响。

翻译技术术语：机器翻译缩写的翻译工作流程