@article{azadi-etal-2026-multiway,
    title = "Multiway Parallel Corpus in Forced Migration Domain for Multilingual Machine Translation",
    author = "Azadi, Fatemeh  and
      Larkin, Samuel  and
      Lo, Chi-kiu",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.384/",
    pages = "4889--4901",
    abstract = "High-quality domain-specific parallel corpora play a significant role in improving the performance of machine translation (MT) and multilingual natural language processing (NLP) systems in a target domain. However, most existing multilingual parallel corpora focus on general-purpose data, and a majority of highly specialized domains such as forced migration are suffering from lack of multilingual data. In this work, we present a new high-quality 4-way parallel corpus in the forced migration domain. The corpus consists of human-translated journal articles from Forced Migration Review in English, French, Spanish, and Arabic. Our corpus contains data aligned at both document and sentence level in four languages and provides a clean and reliable 4-way parallel resource for multilingual research in forced migration. Using this dataset, we benchmark several open-weight large language models (LLMs), an open-weight multilingual MT system, online closed MT systems, and a closed LLM across 12 translation directions. We further leverage our corpus to improve the MT quality of a top-performing multilingual foundation model with two common domain adaptation approaches, fine-tuning and few-shot prompting. Our results demonstrate the effectiveness of our corpus in improving the translation performance of current models in the forced migration domain."
}