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We describe the Sogou neural machine translation systems for the WMT 2017 Chinese↔English news translation tasks. Our systems are based on a multilayer encoder-decoder architecture with attention mechanism. The best translation is obtained with ensemble and reranking techniques. We also propose an approach to improve the named entity translation problem. Our Chinese→English system achieved the highest cased BLEU among all 20 submitted systems, and our English→Chi-nese system ranked the third out of 16 submitted systems.

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This paper focuses on the filtering problems of nonlinear discrete-time stochastic dynamic systems, such as the model simplification, noise characteristics uncertainty, initial conditions uncertainty, or system parametric variation. Under these circumstances, the measurements of system have one sampling time random delay. A new method, that is, strong tracking filtering algorithm of randomly delayed measurements (STF/RDM) for nonlinear systems based on recursive operating by analytical computation and first-order linear approximations, is proposed; a principle of extended orthogonality is presented as a criterion of designing the STF/RDM, and through the residuals between available and predicted measurements, the formula of fading factor is obtained. Under the premise of using the extended orthogonality principle, STF/RDM proposed in this paper can adjust the fading factor online via calculating the covariance of residuals, and then the gain matrices of the STF/RDM adjust in real time to enhance the performance of the proposed method. Lastly, in order to prove that the performance of STF/RDM precedes existing EKF method, the experiment of tracking maneuvering aircraft is carried out.