@inproceedings{montesinos-etal-2026-improving,
    title = "Improving Machine Translation of Idioms: A {S}panish{--}{G}alician Parallel Dataset and Synthetic Augmentation Approach",
    author = "Montesinos, L{\'u}a Santamar{\'i}a  and
      Buj{\'a}n, Sa{\'u}l  and
      Bardanca, Daniel  and
      Gamallo, Pablo",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.99/",
    pages = "980--987",
    ISBN = "979-8-89176-387-6",
    abstract = "Idiomatic expressions are a well-known challenge for neural machine translation, including both traditional sequence-to-sequence models and large language models (LLMs). This paper presents a systematic approach to improve idiom translation between Spanish and Galician. First, we build a high-quality parallel dataset of idioms manually aligned across both languages. Then, we automatically extend this dataset into a large synthetic parallel corpus using LLMs, following a strategy that prioritizes the most frequent idioms observed in authentic corpora. This augmented dataset is used to retrain a seq2seq translation model. We evaluate the resulting system and compare it both to the baseline model without idiom data and to state-of-the-art LLM-based translators such as SalamandraTA. Results show that the translation of idioms improves significantly after the training, alongside a slight boost in the model{'}s overall performance."
}