We Hebben Een Serieus Translatie: Modeling Intercomprehension as Probabilistic Inference

arXiv cs.CL · 18 hours ago

Researchers developed a Bayesian computational model to explain how speakers of one language can partially understand related unfamiliar languages through probabilistic inference. The model uses a language model in the speaker's native language combined with a noise model to infer word mappings, and was tested on human comprehension data from English, Spanish, and Russian speakers attempting to understand Dutch, Italian, and Ukrainian respectively. The approach produced better alignment with human intercomprehension patterns than simpler alternatives and outperformed zero-shot prompting of larger language models.