Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder.
arXiv: Computation and Language(2016)
摘要
In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points. In addition, the approach has achieved interesting and promising results when applied in the translation task that there is no direct parallel corpus between source and target languages.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络