  Automatic morphological processing can aid downstream natural language
processing applications, especially for low-resource languages, and assist
language documentation efforts for endangered languages. Having long been
multilingual, the field of computational morphology is increasingly moving
towards approaches suitable for languages with minimal or no annotated
resources. First, we survey recent developments in computational morphology
with a focus on low-resource languages. Second, we argue that the field is
ready to tackle the logical next challenge: understanding a language's
morphology from raw text alone. We perform an empirical study on a truly
unsupervised version of the paradigm completion task and show that, while
existing state-of-the-art models bridged by two newly proposed models we devise
perform reasonably, there is still much room for improvement. The stakes are
high: solving this task will increase the language coverage of morphological
resources by a number of magnitudes.
