title: Large Scale Multi-Domain Multi-Task Learning with MultiModel
abstract: 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.
authors: Lukasz Kaiser Aidan N. Gomez Noam Shazeer Ashish Vaswani Niki Parmar Llion Jones Jakob Uszkoreit
hIndex: Noam Shazeer 30