Deep learning yields great results across many fields,
 from speech recognition, image classification, to translation.
 But for each problem, getting a deep model to work well involves
 research into the architecture and a long period of tuning.

 We present a single model that yields good results on a number
 of problems spanning multiple domains. In particular, this single model
 is trained concurrently on ImageNet, multiple translation tasks,
 image captioning (COCO dataset), a speech recognition corpus,
 and an English parsing task. 

 Our model architecture incorporates building blocks from multiple
 domains. It contains convolutional layers, an attention mechanism,
 and sparsely-gated layers.

 Each of these computational blocks is crucial for a subset of
 the tasks we train on. Interestingly, even if a block is not
 crucial for a task, we observe that adding it never hurts performance
 and in most cases improves it on all tasks.

 We also show that tasks with less data benefit largely from joint
 training with other tasks, while performance on large tasks degrades
 only slightly if at all.
Large scale multi-task architecture solves ImageNet and translation together and shows transfer learning.