Character-Aware Decoder for Translation into Morphologically Rich Languages

Understand

Neural machine translation (NMT) systems operate primarily on words (or sub-words), ignoring lower-level patterns of morphology.

• We present a character-aware decoder designed to capture such patterns when translating into morphologically rich languages.
• We achieve character-awareness by augmenting both the softmax and embedding layers of an attention-based encoder-decoder model with convolutional neural networks that operate on the spelling of a word.
• To investigate performance on a wide variety of morphological phenomena, we translate English into 14 typologically diverse target languages using the TED multi-target dataset.