Mixed-Level Neural Machine Translation

An Efficient Character-Level Neural Machine Translation

Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-French translation. However, the use of large vocabulary becomes the bottleneck...

Cache-based Document-level Neural Machine Translation

Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text. Current neural machine translation (NMT) systems translate a text in a conventional sentence-by-sentence fashion, ignoring such cross-sentence links and dependencies. This may lead to generate an incohesive and incoherent target text for a cohesive and coherent source text. ...

Towards Document-level Neural Machine Translation

Neural Name Translation Improves Neural Machine Translation

In order to control computational complexity, neural machine translation (NMT) systems convert all rare words outside the vocabulary into a single unk symbol. Previous solution (Luong et al., 2015) resorts to use multiple numbered unks to learn the correspondence between source and target rare words. However, testing words unseen in the training corpus cannot be handled by this method. And it a...

Plan, Attend, Generate: Character-Level Neural Machine Translation with Planning

We investigate the integration of a planning mechanism into an encoder-decoder architecture with attention. We develop a model that can plan ahead when it computes alignments between the source and target sequences not only for a single time-step, but for the next k timesteps as well by constructing a matrix of proposed future alignments and a commitment vector that governs whether to follow or...