Cross-lingual transfer learning has become a cornerstone of multilingual NLP, yet performance disparities persist for low-resource languages, particularly those with typologically divergent features from high-resource source languages. This paper investigates how explicit typological constraints— derived from databases like the World Atlas of Language Structures (WALS) Dryer and Haspelmath [2013]—can guide parameter sharing and alignment in multilingual models. Building on recent work in typologically informed neu…