Extending hybrid word-character neural machine translation with multi-task learning of morphological analysis

Abstract

This article describes the Aalto University entry to the English-to-Finnish news translation shared task in WMT 2017. Our system is an open vocabulary neural machine translation (NMT) system, adapted to the needs of a morphologically complex target language. The main contributions of this paper are 1) implicitly incorporating morphological information to NMT through multi-task learning, 2) adding an attention mechanism to the character-level decoder, combined with character segmentation of names, and 3) a new overattending penalty to beam search.