# Character-Aware Decoder for Translation into Morphologically Rich Languages

arXiv: https://arxiv.org/abs/1809.02223 · HTML: https://mlsanity.com/paper/1809.02223/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Segmentation Beyond Defaults: Asymmetrical Byte Pair Encoding for Optimal Machine Translation Performance](https://mlsanity.com/paper/2511.03383/) (2025)

## Abstract (arXiv)

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. In this low-resource setting, the character-aware decoder provides consistent improvements with BLEU score gains of up to +3.05. In addition, we analyze the relationship between the gains obtained and properties of the target language and find evidence that our model does indeed exploit morphological patterns.