arXiv 2601.086452026-02-24cs.CL

一种平行的跨语言基准用于多模态隐喻性理解

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 … 展开作者

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

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AI总结：

本文提出XMPIE数据集，用于评估多语言多模态隐喻理解能力，包含34种语言和超过一万项内容，支持跨语言和跨模态的隐喻理解研究。

摘要：

潜在隐喻性表达（PIEs）构建的意义与特定语言社区的日常经验紧密相关。因此，它们构成了评估NLP系统语言（在一定程度上也是文化）能力的一个有趣挑战。在本文中，我们提出了XMPIE，一个包含34种语言和超过一万项的平行多语言多模态数据集。该数据集包含34种语言和超过一万项内容，允许在语言特定的实现和偏好之间进行比较分析，以收集关于共享文化方面的见解。这个平行数据集允许对给定PIE在不同语言中的模型性能进行评估，并确定一种语言中的隐喻理解是否可以转移到另一种语言。此外，该数据集支持在文本和视觉模态之间研究PIEs，以衡量一种模态中的PIE理解在另一种模态（文本与图像）中的转移或暗示程度。数据是由语言专家创建的，文本和视觉部分均按照多语言指南进行制作，每个PIE都配有五张图像，这些图像代表从隐喻到字面意义的光谱，包括语义相关和随机干扰项。结果是一个高质量的基准，用于评估多语言和多模态隐喻语言理解。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。