Abstract: A domain fact verification system is described having a computer programmed with a model trained using a process of data distillation and model distillation to improve model learning of the underlying semantics of a dataset rather than relying on statistical and lexical nuances in a domain-specific dataset. The computer thus programmed can accurately perform fact verification across multiple domains without the labor-intensive process of encoding a dataset of human-annotated, domain-specific information for each domain. Moreover, by combining data distillation with model distillation techniques, which may be seen as an inverse of well-established ensemble strategies (which train individual models separately and applies them jointly) the present domain transferable fact verification system scales better at inference time due to its reliance on a single trained model.