H oward U niversity- AI 4 PC at S em E val-2025 Task 2: Improving Machine Translation With Context-Aware Entity-Only Pre-translations with GPT 4o (2025)

Abstract: This paper presents our work on a 3-Step GPT translation system developed for SemEval-2025 Task 2 to enhance the translation of named entities within machine translation. Our approach integrates (1) entity extraction via wikidata, (2) GPT-based refinement of entity translations, and (3) final context-aware GPT translation. Results from the original dataset of six languages show significant improvements in the handling of named entities compared to direct GPT-based translation baselines. We further discuss replicability, observed challenges, and outline future research directions.

## introduction modern translation technologies have enabled cross-cultural communication at scale. additionally, machine translations are the initial step considered in tackling multilingual problems in natural language processing and understanding @xcite . however, translations may lead to the loss of certain linguistic nuances and cultural information @xcite , further contributing to the system's reduced effectiveness. in particular, machine translation (mt) systems often struggle with named entities, particularly rare, ambiguous, or unknown to the translation model. proper handling of names of people, organizations, locations, and products is crucial to maintaining correctness and cultural relevance across different languages. semeval-2025 task 2 @xcite challenges participants to improve named entity translation from english into multiple target languages. we propose a 3-step gpt translation pipeline that integrates external knowledge from wikidata to enrich named entity contexts, combined with carefully constructed gpt prompts. our main contributions include: ## task description semeval-2025 task 2 focuses on accurately translating sentences that contain named entities from english to a set of target languages. these include italian (it), spanish (es), french (fr), german (de), arabic (ar), japanese (ja), chinese (zh), korean (ko), thai (th), and turkish (tr). the task's official scoring metric is the harmonic mean of comet and m-eta. foot_0 ## conclusion and future work we presented a 3-step gpt translation system for semeval-2025 task 2, emphasizing named entity accuracy. by integrating external wikidata and employing carefully engineered gpt prompts across two stages (entity translation refinement and final context-aware translation), our approach achieved notable improvements over baseline gpt-4o minigpt-4o direct translations. despite these promising results, our method still exhibits certain limitations. primarily, our system's performance heavily depends on wikidata coverage; thus, entities without sufficient wikidata entries risk incorrect or incomplete translations. additionally, we identified notably lower performance for languages like chinese due to sparse wikidata coverage and unique script complexities. future research will address these issues by exploring supplementary multilingual knowledge resources or custom lexical databases specifically targeted at improving performance in these languages. we also recognize missed opportunities for prompt customization tailored explicitly to linguistic nuances and specific entity types. therefore, future work will involve developing and testing language-specific and entity-type-specific prompts, aiming to further enhance translation accuracy. finally, addressing computational efficiency remains a critical component of future improvements. we plan to investigate optimization strategies such as single-pass gpt prompts or selectively triggered gpt calls, dynamically invoking gpt only when entity translations are uncertain or when comprehensive wikidata coverage is lacking. these optimizations will aim to sustain high accuracy while significantly reducing computational resources and translation latency.