A Parallel Cross-Lingual Benchmark for Multimodal Idiomaticity Understanding
Dilara Torunoğlu-Selamet, Dogukan Arslan, Rodrigo Wilkens, Wei He, Doruk Eryiğit, Thomas Pickard, Adriana S. Pagano, Aline Villavicencio, Gülşen Eryiğit, Ágnes Abuczki, Aida Cardoso, Alesia Lazarenka, Dina Almassova, Amalia Mendes, Anna Kanellopoulou, Antoni Brosa-Rodríguez, Baiba Saulite, Beata Wojtowicz, Bolette Pedersen, Carlos Manuel Hidalgo-Ternero, Chaya Liebeskind, Danka Jokić, Diego Alves, Eleni Triantafyllidi, Erik Velldal, Fred Philippy, Giedre Valunaite Oleskeviciene, Ieva Rizgeliene, Inguna Skadina, Irina Lobzhanidze, Isabell Stinessen Haugen, Jauza Akbar Krito, Jelena M. Marković, Johanna Monti, Josue Alejandro Sauca, Kaja Dobrovoljc, Kingsley O. Ugwuanyi, Laura Rituma, Lilja Øvrelid, Maha Tufail Agro, Manzura Abjalova, Maria Chatzigrigoriou, María del Mar Sánchez Ramos, Marija Pendevska, Masoumeh Seyyedrezaei, Mehrnoush Shamsfard, Momina Ahsan, Muhammad Ahsan Riaz Khan, Nathalie Carmen Hau Norman, Nilay Erdem Ayyıldız, Nina Hosseini-Kivanani, Noémi Ligeti-Nagy, Numaan Naeem, Olha Kanishcheva, Olha Yatsyshyna, Daniil Orel, Petra Giommarelli, Petya Osenova, Radovan Garabik, Regina E. Semou, Rozane Rebechi, Salsabila Zahirah Pranida, Samia Touileb, Sanni Nimb, Sarfraz Ahmad, Sarvinoz Sharipova, Shahar Golan, Shaoxiong Ji, Sopuruchi Christian Aboh, Srdjan Sucur, Stella Markantonatou, Sussi Olsen, Vahide Tajalli, Veronika Lipp, Voula Giouli, Yelda Yeşildal Eraydın, Zahra Saaberi, Zhuohan Xie · Jan 13, 2026 · Citations: 0
Abstract
Potentially idiomatic expressions (PIEs) construe meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challenge for assessing the linguistic (and to some extent cultural) capabilities of NLP systems. In this paper, we present XMPIE, a parallel multilingual and multimodal dataset of potentially idiomatic expressions. The dataset, containing 34 languages and over ten thousand items, allows comparative analyses of idiomatic patterns among language-specific realisations and preferences in order to gather insights about shared cultural aspects. This parallel dataset allows to evaluate model performance for a given PIE in different languages and whether idiomatic understanding in one language can be transferred to another. Moreover, the dataset supports the study of PIEs across textual and visual modalities, to measure to what extent PIE understanding in one modality transfers or implies in understanding in another modality (text vs. image). The data was created by language experts, with both textual and visual components crafted under multilingual guidelines, and each PIE is accompanied by five images representing a spectrum from idiomatic to literal meanings, including semantically related and random distractors. The result is a high-quality benchmark for evaluating multilingual and multimodal idiomatic language understanding.