  Neural Machine Translation (NMT) in low-resource settings and of
morphologically rich languages is made difficult in part by data sparsity of
vocabulary words. Several methods have been used to help reduce this sparsity,
notably Byte-Pair Encoding (BPE) and a character-based CNN layer (charCNN).
However, the charCNN has largely been neglected, possibly because it has only
been compared to BPE rather than combined with it. We argue for a
reconsideration of the charCNN, based on cross-lingual improvements on
low-resource data. We translate from 8 languages into English, using a
multi-way parallel collection of TED transcripts. We find that in most cases,
using both BPE and a charCNN performs best, while in Hebrew, using a charCNN
over words is best.
