改进机器翻译中缩写的歧义消解，提升法英翻译准确率。
On Translating Technical Terminology: A Translation Workflow for Machine-Translated Acronyms
- 构建公开缩写词库，辅助翻译系统识别术语
- 搜索阈值算法使准确率比谷歌翻译高近10%
- 适合需要精准术语翻译的科技文档译者
主流机器翻译系统在处理技术术语（特别是缩写）时存在明显不足。我们发现，当前公开可用的翻译工具如谷歌翻译在处理缩写时错误率高达50%。本文针对法语到英语的翻译流程提出改进方案：首先发布一个公开可获取的新缩写词库，随后引入基于搜索的阈值算法，在相同测试集上相比谷歌翻译和OpusMT实现近10%的准确率提升。该方法显著提升了技术文档中缩写的翻译质量。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。