Neural machine translation (NMT) heavily relies on context vectors generated by an attention network to predict target words. In practice, we observe that the context vectors for different target words are quite similar to one another and translations with such nondiscriminatory context vectors tend to be degenerative. We ascribe this similarity to the invariant source representations that lack dynamics across decoding steps. In this article, we propose a novel gated recurrent unit (GRU)-gated attention model (GAtt) for NMT. By updating the source representations with the previous decoder state via a GRU, GAtt enables translation-sensitive source representations that then contribute to discriminative context vectors. We further propose a variant of GAtt by swapping the input order of the source representations and the previous decoder state to the GRU. Experiments on the NIST Chinese-English, WMT14 English-German, and WMT17 English-German translation tasks show that the two GAtt models achieve significant improvements over the vanilla attention-based NMT. Further analyses on the attention weights and context vectors demonstrate the effectiveness of GAtt in enhancing the discriminating capacity of representations and handling the challenging issue of overtranslation.

Deepening neural models has been proven very successful in improving the model's capacity when solving complex learning tasks, such as the machine translation task. Previous efforts on deep neural machine translation mainly focus on the encoder and the decoder, while little on the attention mechanism. However, the attention mechanism is of vital importance to induce the translation correspondence between different languages where shallow neural networks are relatively insufficient, especially when the encoder and decoder are deep. In this paper, we propose a deep attention model (DeepAtt). Based on the low-level attention information, DeepAtt is capable of automatically determining what should be passed or suppressed from the corresponding encoder layer so as to make the distributed representation appropriate for high-level attention and translation. We conduct experiments on NIST Chinese-English, WMT English-German and WMT English-French translation tasks, where, with 5 attention layers, DeepAtt yields very competitive performance against the state-of-the-art results. We empirically find that with an adequate increase of attention layers, DeepAtt tends to produce more accurate attention weights. An in-depth analysis on the translation of important context words further reveals that DeepAtt significantly improves the faithfulness of system translations.

The predicted age difference (PAD) between an individual's predicted brain age and chronological age has been commonly viewed as a meaningful phenotype relating to aging and brain diseases. However, the systematic bias appears in the PAD achieved using machine learning methods. Recent studies have designed diverse bias correction methods to eliminate it for further downstream studies. Strikingly, here we demonstrate that bias still exists in the PAD of samples with the same age even after kind of correction. Therefore, current PAD may not be taken as a reliable phenotype and more investigations are needed to solve this fundamental defect. To this end, we propose an age-level bias correction method and demonstrate its efficacy in numerical experiments.

Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper. Different from the variational NMT, VRNMT introduces a series of latent random variables to model the translation procedure of a sentence in a generative way, instead of a single latent variable. Specifically, the latent random variables are included into the hidden states of the NMT decoder with elements from the variational autoencoder. In this way, these variables are recurrently generated, which enables them to further capture strong and complex dependencies among the output translations at different timesteps. In order to deal with the challenges in performing efficient posterior inference and large-scale training during the incorporation of latent variables, we build a neural posterior approximator, and equip it with a reparameterization technique to estimate the variational lower bound. Experiments on Chinese-English and English-German translation tasks demonstrate that the proposed model achieves significant improvements over both the conventional and variational NMT models.