Recently, neural machine translation (NMT) has emerged as a powerful
alternative to conventional statistical approaches. However, its performance
drops considerably in the presence of morphologically rich languages (MRLs).
Neural engines usually fail to tackle the large vocabulary and high
out-of-vocabulary (OOV) word rate of MRLs. Therefore, it is not suitable to
exploit existing word-based models to translate this set of languages. In this
paper, we propose an extension to the state-of-the-art model of Chung et al.
(2016), which works at the character level and boosts the decoder with
target-side morphological information. In our architecture, an additional
morphology table is plugged into the model. Each time the decoder samples from
a target vocabulary, the table sends auxiliary signals from the most relevant
affixes in order to enrich the decoder's current state and constrain it to
provide better predictions. We evaluated our model to translate English into
German, Russian, and Turkish as three MRLs and observed significant
improvements.

该研究论文提出了一种基于字符级别的神经机器翻译方法，引入了目标语言的形态信息加强解码器的性能，适用于翻译形态丰富的语言，如德语、俄语和土耳其语，并通过实验证明了其优越性。