On Translating Technical Terminology: A Translation Workflow for Machine-Translated Acronyms
This addresses a specific bottleneck for translators and NLP practitioners by improving acronym disambiguation in machine translation, though it is an incremental advance focused on a narrow domain.
The paper tackles the problem of inaccurate translation of technical acronyms in machine translation systems, finding errors in up to 50% of cases, and proposes a search-based thresholding algorithm that improves accuracy by nearly 10% compared to existing systems like Google Translate and OpusMT.
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.