Data Augmentation for Morphological Reinflection
Miikka Silfverberg, Adam Wiemerslage, Ling Liu, Lingshuang Jack Mao · 2017
This paper presents the submission of the Linguistics Department of the University of Colorado at Boulder for the 2017 CoNLL-SIGMORPHON Shared Task on Universal Morphological Reinflection.The system is implemented as an RNN Encoder-Decoder.It is specifically geared toward a low-resource setting.To this end, it employs data augmentation for counteracting overfitting and a copy symbol for processing characters unseen in the training data.The system is an ensemble of ten models combined using a weighted voting scheme.It delivers substantial improvement in accuracy compared to a non-neural baseline system in presence of varying amounts of training data.