This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models.
Moreover, it is difficult for user to jump out of their specific historical behaviors for possible interest exploration, namely weak generalization problem.
We propose a novel weakly supervised approach for 3D semantic segmentation on volumetric images.
This provides the first benchmark for quantitative evaluation of models to assess building damage using aerial videos.
In the real traffic of a large-scale e-commerce sponsored search, the proposed approach significantly outperforms the baseline.