Attention-free encoder decoder for morphological processing

Abstract

We present RACAI’s Entry for the CoNLL–SIGMORPHON 2018 shared task on universal morphological reinflection. The system is based on an attention-free encoder-decoder neural architecture with a bidirectional LSTM for encoding the input sequence and a unidirectional LSTM for decoding and producing the output. Instead of directly applying a sequence-to-sequence model at character-level we use a dynamic algorithm to align the input and output sequences. Based on these alignments we produce a series of special symbols which are similar to those of a finite-state-transducer (FST).