Does Typological Blinding Impede Cross-Lingual Sharing?
Johannes Bjerva, Isabelle Augenstein · 2021
Bridging the performance gap between highand low-resource languages has been the focus of much previous work.Typological features from databases such as the World Atlas of Language Structures (WALS) are a prime candidate for this, as such data exists even for very low-resource languages.However, previous work has only found minor benefits from using typological information.Our hypothesis is that a model trained in a cross-lingual setting will pick up on typological cues from the input data, thus overshadowing the utility of explicitly using such features.We verify this hypothesis by blinding a model to typological information, and investigate how cross-lingual sharing and performance is impacted.Our model is based on a cross-lingual architecture in which the latent weights governing the sharing between languages is learnt during training.We show that (i) preventing this model from exploiting typology severely reduces performance, while a control experiment reaffirms that (ii) encouraging sharing according to typology somewhat improves performance.