Midheind at WMT 25 General Machine Translation Task (2025)

Abstract: We present Midheind’s system contribution to two tasks at WMT25 – Tenth Conference on Machine Translation: The General Machine Translation Task and the WMT25 Terminology Shared Task. Erlendur is a multilingual LLM-based translation system that employs a multi-stage pipeline approach, with enhancements especially for translations from English to Icelandic. We address translation quality and grammatical accuracy challenges in current LLMs through a hybrid prompt-based approach that can benefit lower-resource language pairs. In a preparatory step, the LLM analyzes the source text and extracts key terms for lookup in an English-Icelandic dictionary. The findings of the analysis and the retrieved dictionary results are then incorporated into the translation prompt. When provided with a custom glossary, the system identifies relevant terms from the glossary and incorporates them into the translation, to ensure consistency in terminology. For longer inputs, the system maintains translation consistency by providing contextual information from preceding text chunks. Lastly, Icelandic target texts are passed through our custom-developed seq2seq language correction model (Ingólfsdóttir et al., 2023), where grammatical errors are corrected. Using this hybrid method, Erlendur delivers high-quality translations, without fine-tuning. Erlendur ranked 3rd-4th overall in the General Machine Translation Task for English-Icelandic translations, achieving the highest rank amongst all systems submitted by WMT25 participants (Kocmi et al., 2025a). Notably, in the WMT25 Terminology Shared Task, Erlendur placed 3rd in Track 1 and took first place in the more demanding Track 2 (Semenov et al., 2025).

## introduction while large language models (llms) exhibit strong cross-lingual understanding and can produce high-quality translations from lower-resource languages into major languages like english, gaps in the models' vocabulary and limitations in their grammatical knowledge @xcite often become apparent when translating into lowerresource languages @xcite . here we describe erlendur, a multilingual llm-based translation system designed to address these challenges by enhancing the quality and grammaticality of icelandic translations. our main contribution is a hybrid, multi-stage pipeline that combines preparatory text analysis, dictionary lookup, glossary integration, careful prompting, seamless handling of longer texts, and grammatical error correction. we deployed erlendur for our submissions to wmt25. in the general machine translation task (unconstrained track) foot_0 for english-icelandic translations, erlendur achieved the highest performance among participating systems for the language pair, ranking 3rd overall behind a human translation (1st) and gemini 2.5 pro (2nd). erlendur marginally outperformed gpt-4.1, though within the margin of statistical significance @xcite . in the wmt25 terminology translation task foot_1 , where participating systems must correctly incorporate glossary terms into their translations, erlendur placed third in track 1, which tests injection of glossary terms into short text chunks, and secured first place in track 2, which tests the scalability of the terminology approach, with much longer glossaries and corpus-level texts @xcite . ## system description erlendur (see figure 1 ) is a translation service provided through málstaður foot_2 @xcite , an integrated platform for language technology solutions aimed at icelandic. users can access translation capabilities through málstaður's editor interface, or through a speech recognition system where they can speak directly and receive translations of the transcribed text. the translation service is also provided commercially through an api. all the enhancement measures described in this section are intended to improve the translation capabilities beyond a baseline llm translation. these measures are language-agnostic, unless otherwise stated. the modular nature of the system allows for most of these to be turned on and off. for the wmt25 submissions, all enhancements were used. ## wmt submissions we used erlendur for both the general and the terminology shared tasks, with the same enhancements. the following sections describe the details of each submission. ## conclusion we have presented erlendur, a multilingual llmbased translation system that addresses the challenges of translating into lower-resource languages through a hybrid multi-stage approach. by combining preparatory text analysis, dictionary lookup, glossary integration, and grammatical error correction, the system demonstrates how targeted enhancements can significantly improve translation quality for languages like icelandic without requiring model fine-tuning. the language-agnostic design of most system components makes this approach applicable to other lower-resource language pairs, while the modular architecture allows for flexible adaptation to different translation scenarios and future model improvements.