Culture-aware machine translation: the case study of low-resource language pair C atalan- C hinese (2025)

Abstract: High-quality machine translation requires datasets that not only ensure linguistic accuracy but also capture regional and cultural nuances. While many existing benchmarks, such as FLORES-200, rely on English as a pivot language, this approach can overlook the specificity of direct language pairs, particularly for underrepresented combinations like Catalan-Chinese. In this study, we demonstrate that even with a relatively small dataset of approximately 1,000 sentences, we can significantly improve MT localization. To this end, we introduce a dataset specifically designed to enhance Catalan-to-Chinese translation by prioritizing regionally and culturally specific topics. Unlike pivot-based datasets, our data source ensures a more faithful representation of Catalan linguistic and cultural elements, leading to more accurate translations of local terms and expressions. Using this dataset, we demonstrate better performance over the English-pivot FLORES-200 dev set and achieve competitive results on the FLORES-200 devtest set when evaluated with neural-based metrics. We release this dataset as both a human-preference resource and a benchmark for Catalan-Chinese translation. Additionally, we include Spanish translations for each sentence, facilitating extensions to Spanish-Chinese translation tasks.

## introduction in recent years, the field of neural machine translation (nmt) has seen substantial progress in the development of multilingual models, which can translate across multiple languages as a single unified model (e.g., @xcite @xcite @xcite , as well as the creation of human-translated multilingual benchmark datasets (e.g., @xcite . these advancements have pushed the boundaries of many-to-many translation capabilities. however, practical applications often require systems to be tailored to specific cultural and regional contexts (e.g., @xcite . one particularly challenging area is the translation of texts that contain entity names, as culturalrelated references can vary significantly across languages @xcite . translating names between languages with different scripts, such as latin and logographic (e.g., chinese), also involves transliteration to maintain ease of pronunciation and closeness to the original sound. sometimes, the same name can even yield different transliterations based on the source language's pronunciation. for example, the name josé is transliterated as 若 泽(ruò zé) from portuguese to chinese, but from spanish, it becomes 何塞(hé sài). therefore, we need to adapt the many-to-many system to be more language-and culture-specific. this study focuses on the catalan-to-chinese (@xmath0, a relatively underexplored area despite its growing relevance given the deepening economic and cultural connections between catalonia and china. chinese speakers form one of the five largest immigrant communities in catalonia, where catalan is an official language. 1 besides, china is also catalonia's third-largest non-european investor and the top source of non-european, non-english-speaking tourists. 2 these growing interactions underline the urgent need for effective translation tools to facilitate communication and foster collaboration between catalan and chinese speakers. despite its significance, developing robust ca-zh mt systems remains challeng-ing due to the limited availability of high-quality parallel datasets. in this study, we address the problem of adapting multilingual nmt models to @xmath1. more specifically, the contributions of our work are as follows: ## dataset construction cpo requires a preference dataset, consisting of a "prompt", a "chosen" completion, and a "rejected" completion. the objective is to train the model to prefer the "chosen" response over the "rejected" response. this section describes the construction of our preference datasets. specifically, we create two datasets to assess the effects of using different types of data in cpo: below, we describe the data sourcing process for cpo ca-zh, the translation methodology, and a more detailed composition of the two preference datasets. ## entities in the cpo ca-zh dataset to analyze the key entities discussed in our cpo ca-zh dataset, we used spacy (version 3.8.3) to extract proper noun phrases and their corresponding frequencies from the catalan sentences. these entities were then compared with those in the flores-200 and ntrex @xcite to assess how the topics in our dataset differ from those in existing datasets. overall, the cpo ca-zh dataset is more focused on geographically specific topics, with frequent references to entities such as barcelona, espanya (spain), and catalunya (catalonia). these entities are either absent or significantly less prominent in the other datasets. in contrast, the dev and devtest splits of the flores-200 prominently feature estats units (united states) as the most frequent entity, and the ntrex also tends to focus more often on entities like trump and usa. for a complete comparison, see ## experiments we applied cpo to the m2m-100-1.2b model using each of the two preference datasets introduced in section 3.3. to assess the models after cpo training, we evaluated their translation performance on the flores-200 devtest split, which primarily focuses on topics relevant to the united states and global contexts. in addition, we conducted a/b testing on translations of 100 sentences containing localized terms specific to catalonia. this allowed us to evaluate and compare the models' capabilities in handling more region-specific translations. ## conclusion many existing machine translation benchmarks, such as flores-200, rely on english as a pivot language for non-english language pairs. this approach can overlook the linguistic and cultural specificity of direct translations, particularly for language pairs like catalan-chinese (ca-zh), where structural differences, idiomatic expressions, and cultural references may not have direct equivalents in english. to address this gap, we present a ca-zh parallel dataset containing 1,022 sentences sourced from catalan and spanish wikimedia and directly translated into mandarin chinese. unlike most existing benchmarks, our dataset prioritizes linguistic and cultural authenticity by capturing regional nuances specific to catalonia and spain. this localization ensures that translations reflect real-world usage rather than being filtered through a more globalized or english-centric lens. by com-paring our dataset to the flores-200 dev set, we demonstrate the benefits of aligning machine translation (mt) systems with culturally and regionally grounded data. this direct translation approach outperforms english-pivoted methods, which often introduce biases from the englishspeaking world. additionally, our dataset enables more accurate pronunciation mapping and transliteration between catalan and chinese, further improving transliteration quality for practical applications. our work highlights the importance of developing non-english-centric datasets to better serve low-resource language pairs. we hope that the release of this dataset will encourage further research into localized, culturally rich resources and improve mt systems for real-world use.