This paper presents MED, the main system of the LMU team for the SIGMORPHON 2016 Shared Task on Morphological Reinflection as well as an extended analysis of how different design choices contribute to the final performance. We model the task of morphological reinflection using neural encoder-decoder models together with an encoding of the input as a single sequence of the morphological tags of the source and target form as well as the sequence of letters of the source form. The Shared Task consists of three subtasks, three different tracks and covers 10 different languages to encourage the use of language-independent approaches. MED was the system with the overall best performance, demonstrating our method generalizes well for the low-resource setting of the SIGMORPHON 2016 Shared Task.
['Katharina Kann', 'Hinrich Schütze']
MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection