  This paper describes the Stockholm University/University of Groningen
(SU-RUG) system for the SIGMORPHON 2017 shared task on morphological
inflection. Our system is based on an attentional sequence-to-sequence neural
network model using Long Short-Term Memory (LSTM) cells, with joint training of
morphological inflection and the inverse transformation, i.e. lemmatization and
morphological analysis. Our system outperforms the baseline with a large
margin, and our submission ranks as the 4th best team for the track we
participate in (task 1, high-resource).
