Papers by Matteo Salloum
Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+ (2026.eacl-srw)
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York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum, Michael Zhou, Phuong Hanh Hoang, A. Seza Doğruöz, En-Shiun Annie Lee
| Challenge: | Existing linguistic knowledge bases such as URIEL+ lack a principled method for aggregating these signals into a single, comprehensive score. |
| Approach: | They propose a framework for type-matched language distances that unifies these signals into a robust, task-agnostic composite distance. |
| Outcome: | The proposed representations improve transfer performance when the distance type is relevant to the task, while yielding gains in most tasks. |