Abstractive morphological learning with a recurrent neural network

In traditional word-and-paradigm models of morphology, an inflectional system is represented via a set of exemplary paradigms. Novel wordforms are produced by analogy with previously encountered forms. This paper describes a recurrent neural network which can use this strategy to learn the paradigms of a morphologically complex language based on incomplete and randomized input. Results are given which show good performance for a range of typologically diverse languages.