Deconvolution-Based Global Decoding for Neural Machine Translation
This addresses the problem of inefficient and error-prone sequential decoding in NMT for translation tasks, though it appears incremental as it builds on existing Seq2Seq frameworks.
The paper tackles the limitation of sequential decoding in neural machine translation by proposing a model that uses structural prediction of the target-side context to guide translation, freeing it from sequential order. Experimental results show the model is competitive with state-of-the-art methods, robust across sentence lengths, and reduces repetition.
A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. As the studies of linguistics have proved that language is not linear word sequence but sequence of complex structure, translation at each step should be conditioned on the whole target-side context. To tackle the problem, we propose a new NMT model that decodes the sequence with the guidance of its structural prediction of the context of the target sequence. Our model generates translation based on the structural prediction of the target-side context so that the translation can be freed from the bind of sequential order. Experimental results demonstrate that our model is more competitive compared with the state-of-the-art methods, and the analysis reflects that our model is also robust to translating sentences of different lengths and it also reduces repetition with the instruction from the target-side context for decoding.