On both the tasks of morphological tagging and lemmatization, neural models have supplanted linear models in terms of performance in the high-resource case .
we are interested in producing an accurate approximation to the posterior in the presence of minimal annotated examples and potentially noisy samples produced during the sleep phase, where linear models still outperform non-linear approaches (Cotterell and Heigold, 2017).