It has been shown that training multi-task models with auxiliary tasks can improve the target task quality through cross-task transfer. However, the importance of each auxiliary task to the primary task is likely not known a priori. While the importance weights of auxiliary tasks can be manually tuned, it becomes practically infeasible with the number of tasks scaling up. To address this, we propose a search method that automatically assigns importance weights. We formulate it as a reinforcement learning problem and learn a task sampling schedule based on the evaluation accuracy of the multi-task model. Our empirical evaluation on XNLI and GLUE shows that our method outperforms uniform sampling and the corresponding single-task baseline.

Scale has played a central role in the rapid progress natural language processing has enjoyed in recent years. While benchmarks are dominated by ever larger models, efficient hardware use is critical for their widespread adoption and further progress in the field. In this cutting-edge tutorial, we will recapitulate the state-of-the-art in natural language processing with scale in perspective. After establishing these foundations, we will cover a wide range of techniques for improving efficiency, including knowledge distillation, quantization, pruning, more efficient architectures, along with case studies and practical implementation tricks.