The Impact of Domain-Specific Terminology on Machine Translation for Finance in E uropean Languages (2025)

Abstract: Domain-specific machine translation (MT) poses significant challenges due to specialized terminology, particularly when translating across multiple languages with scarce resources. In this study, we present the first impact analysis of domain-specific terminology on multilingual MT for finance, focusing on European languages within the subdomain of macroeconomics. To this end, we construct a multi-parallel corpus from the European Central Bank, aligned across 22 languages. Using this resource, we compare open-source multilingual MT systems with large language models (LLMs) that possess multilingual capabilities. Furthermore, by developing and curating an English financial glossary, we propose a methodology to analyze the relationship between translation performance (into English) and the accuracy of financial term matching, obtaining significant correlation results. Finally, using the multi-parallel corpus and the English glossary, we automatically align a multilingual financial terminology, validating the English-Spanish alignments and incorporating them into our discussion. Our findings provide valuable insights into the current state of financial MT for European languages and offer resources for future research and system improvements.

## introduction in the age of globalization, mt plays a crucial role in facilitating cross-linguistic communication in different domains such as finance. accurate translation of financial documents is essential for informed decision-making, regulatory compliance, and international collaboration @xcite . however, financial language is characterized by specialized terminology, complex sentence structures, and domain-specific conventions, which present significant challenges for mt systems @xcite . despite the growing relevance of mt in finance, publicly available datasets for evaluating mt systems in this domain remain limited. existing resources often lack linguistic diversity or domainspecific materials @xcite , such as terminologies, needed to comprehensively assess translation performance in financial contexts. in this work, we introduce a new multi-parallel corpus from the european central bank (ecb) foot_1 , consisting of financial (macroeconomics) articles published exclusively in 2024, translated and aligned across 22 european languages. using this resource, we can evaluate how mt systems handle the complexities of financial translation, particularly domain-specific terminology. given the recent advancements of robust llms for generation tasks, such as llama 3 (llama team, 2024), or models with strong multilingual capabilities like aya23 @xcite , it is essential to assess their performance against large-scale multilingual mt systems, such as nllb-200 @xcite and madlad-400 @xcite . to address this, we propose a methodology for analyzing the impact of domain-specific financial terminology on mt performance. by developing and curating an english-language glossary of financial terms, we assess how terminology accuracy influences translation performance at both the language and segment-levels. additionally, we automatically align a multilingual financial terminology, which we use to complement our discussion. our contributions are threefold: ## multi-parallel financial dataset in this section, we describe the creation of a new multi-parallel financial dataset sourced from the ecb, covering macroeconomic topics. we refer to it as multi-parallel because it is aligned at the segment-level foot_2 across 22 languages from the indo-european and uralic language families (see the full list in ## benchmarking financial mt after constructing the multi-parallel corpus, we evaluate the translation performance of various open-source multilingual mt systems and llms. we limit our evaluation to open-source models with a traceable knowledge cut-off date (2023 or earlier) to limit data contamination. it is important to clarify that our goal is not to identify or fine-tune the best model for this dataset. instead, we aim to compare the challenges that different models face when translating financial content and to analyze the impact of financial terminology (which will be explored further in the next section). models for mt systems, we evaluate two models: nllb-200 (3.3b params.) @xcite and madlad-400 (3b params.) @xcite . both models are designed for high-performance multilingual translation tasks and cover a wide range of languages. for llms, we evaluate three models: llama3-instruct (8b params.) @xcite , which has demonstrated robust performance across inference we use an nvidia a10g gpu for all models. for the mt systems, we set the max new token up to 512, and 1024 for the llms, as we have long segments up to 400 words in english. llms use 0.3 as temperature, and the translation prompt is shown in ## the challenge of terminology in financial mt building on our previous analysis of general translation performance, we now focus on the challenge of accurately translating financial terminology. our methodology is as follows: first, we extract a financial terminology in english (see §5.1), and in the @xmath0, we can compute the term match accuracy for the predicted english outputs (see §5.2). then, we introduce our analysis for assessing the relationship between term match accuracy and automatic evaluation metrics for translation, which is conducted at the corpus and segmentlevel (see §5.3). finally, using the multi-parallel dataset and english glossary, we align a multilingual financial terminology (see §5.4). ## conclusion to the best of our knowledge, this is the first systematic study to analyze the impact of domain-specific terminology on translation performance across 22 european languages in the financial domain, where we observed significant correlations between term accuracy and overall translation quality at both the corpus and segment levels. this work involved creating a multi-parallel financial corpus aligned at the segment-level, evaluating the translation performance of diverse mt systems and llms, and curating an english financial terminology. we also laid the groundwork for building a multilingual financial terminology, which will be a valuable resource for advancing financial mt.