@inproceedings{almheiri-etal-2026-multilingual,
    title = "Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages",
    author = "Almheiri, Saeed  and
      Elbouardi, Bilal  and
      Pranida, Salsabila Zahirah  and
      Nikishina, Irina  and
      B, Ashwath Rao  and
      Krishnamurthy, Parameswari  and
      Airlangga, Muhammad Cendekia  and
      Genadi, Rifo Ahmad  and
      Bao, Nguyen Phan Gia  and
      Yari, Amir Hossein  and
      Toyin, Hawau Olamide  and
      Mukhituly, Nurdaulet  and
      Attia, Mena  and
      Hassan, Besher  and
      Hidayatullah, Ahmad Fathan  and
      Kuribayashi, Tatsuki  and
      Li, Haonan  and
      Bhat, Suma  and
      Koto, Fajri",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.564/",
    pages = "12363--12389",
    ISBN = "979-8-89176-390-6",
    abstract = "Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. Prior work has focused on high-resource languages typically evaluates isolated idiom-meaning questions, overlooking realistic discourse. We introduce MIDI, a multilingual idiom dataset spanning 3 high-, 3 medium-, and 12 low-resource languages, curated by native speakers. Unlike previous datasets, MIDI provides idioms embedded in both sentence-level and conversational contexts, capturing both literal and figurative readings. Benchmarking state-of-the-art models shows that idiom comprehension degrades in low-resource languages and that, in all resource tiers, literal interpretations are substantially harder than figurative ones. Conversational context improves performance but does not eliminate these disparities. Through controlled tests and interventions on hidden representations, we further separate memorization from reasoning, exposing core limitations of current models."
}