This research explores Cultural Transcreation (CT) for East Asian languages, focusing primarily on Mandarin Chinese (ZH) and the customer service (CS) market. We combined Large Language Models (LLMs) with prompt engineering to develop a CT product that, aligned with the Augmented Translation concept, enhances multilingual CS communication, enables professionals to engage with their target audience effortlessly, and improves overall service quality. Through a series of preparatory steps, including guideline establishment, benchmark validation, iterative prompt refinement, and LLM testing, we integrated the CT product into the CS platform, assessed its performance, and refined prompts based on a pilot feedback. The results highlight its success in empowering agents, regardless of linguistic or cultural expertise, to bridge effective communication gaps through AI-assisted cultural rephrasing, thus achieving its market launch. Beyond CS, the study extends the concept of transcreation and prompt-based LLM applications to other fields, discussing its performance in the language conversion of website content and advertising.

We present how at Unbabel we have been using Large Language Models to apply a Cultural Transcreation (CT) product on customer support (CS) emails and how we have been testing the quality and potential of this product. We discuss our preliminary evaluation of the performance of different MT models in the task of translating rephrased content and the quality of the translation outputs. Furthermore, we introduce the live pilot programme and the corresponding relevant findings, showing that transcreated content is not only culturally adequate but it is also of high rephrasing and translation quality.

While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies. Understanding these errors can potentially help improve the translation quality and user experience. This paper introduces xTower, an open large language model (LLM) built on top of TowerBase designed to provide free-text explanations for translation errors in order to guide the generation of a corrected translation. The quality of the generated explanations by xTower are assessed via both intrinsic and extrinsic evaluation. We ask expert translators to evaluate the quality of the explanations across two dimensions: relatedness towards the error span being explained and helpfulness in error understanding and improving translation quality. Extrinsically, we test xTower across various experimental setups in generating translation corrections, demonstrating significant improvements in translation quality. Our findings highlight xTower’s potential towards not only producing plausible and helpful explanations of automatic translations, but also leveraging them to suggest corrected translations.

In this work, we present Tower v2, an improved iteration of the state-of-the-art open-weight Tower models, and the backbone of our submission to the WMT24 General Translation shared task. Tower v2 introduces key improvements including expanded language coverage, enhanced data quality, and increased model capacity up to 70B parameters. Our final submission combines these advancements with quality-aware decoding strategies, selecting translations based on multiple translation quality signals. The resulting system demonstrates significant improvement over previous versions, outperforming closed commercial systems like GPT-4o, Claude 3.5, and DeepL even at a smaller 7B scale.