Reviews: Neural Machine Translation with Soft Prototype

Neural Machine Translation with Soft Prototype The paper suggests to equip a neural machine translation system with a soft prototype in order to provide global information when generating the target sequence. The suggested approach shares similarities with a multi-pass decoding strategy such as in deliberation networks, however, with the difference that the prototype is not a hard sequence of tokens but a soft representation. To achieve fast inference speed and only a small increase in terms of model parameters compared to the baseline system, the authors share the parameters between the Encoder network and the additional network used to encode the soft prototype. Experiments are conducted for three different setups on the WMT EnDe and EnFr tasks: a supervised, a semi-supervised and an unsupervised setting. The proposed technique yields gains between 0.3 and 1.0 BLEU points depending on the setup over their corresponding baselines and are claimed to achieve new state-of-the-art results.