Timely and reliable multilingual communication is critical during natural and human-induced disasters, but developing effective solutions for crisis communication is limited by the scarcity of curated parallel data. We propose a domain-adaptive pipeline that expands a small reference corpus, by retrieving and filtering data from general corpora. We use the resulting dataset to fine-tune a small language model for crisis-domain translation and then apply preference optimization to bias outputs toward CEFR A2-level English. Automatic and human evaluation shows that this approach improves readability, while maintaining strong adequacy. Our results indicate that simplified English, combined with domain adaptation, can function as a practical lingua franca for emergency communication when full multilingual coverage is not feasible.

This paper presents the outcomes of an initial investigation into the performance of Large Language Models (LLMs) and Neural Machine Translation (NMT) systems in translating high-stakes messages. The research employed a novel bilingual corpus, ITALERT (Italian Emergency Response Text) and applied a human-centric post-editing based metric (HOPE) to assess translation quality systematically. The initial dataset contains eleven texts in Italian and their corresponding English translations, both extracted from the national communication campaign website of the Italian Civil Protection Department. The texts deal with eight crisis scenarios: flooding, earthquake, forest fire, volcanic eruption, tsunami, industrial accident, nuclear risk, and dam failure. The dataset has been carefully compiled to ensure usability and clarity for evaluating machine translation (MT) systems in crisis settings. Our findings show that current LLMs and NMT models, such as ChatGPT (OpenAI’s GPT-4o model) and Google MT, face limitations in translating emergency texts, particularly in maintaining the appropriate register, resolving context ambiguities, and managing domain-specific terminology.

This paper presents an AI experiment of translation in emoji conducted on a glossary from Dante Alighieri’s Comedy. The experiment is part of a project aiming to build up an automated emojibased pivot language providing an interlingua as a tool for linguistic simplification, accessibility, and international communication: Emojilingo. The present test involves human (Emojitaliano) and machine (Chat-GPT) translations in a comparative analysis to devise an automated integrated model highlighting emojis’ expressive ability in transferring senses, clarifying semantic obscurities and ambiguities, and simplifying language. A first preliminary evaluation highlights Chat-GPT’s ability to deal with a classic archaic literary vocabulary, also raising issues on managing criteria for better grasping the meanings and forms and about the multicultural extent of content transfer.