Google Research

Learning Task Sampling Policy for Multitask Learning

  • Dhanasekar Sundararaman
  • Henry Tsai
  • Kuang-Huei Lee
  • Iulia Raluca Turc
  • Lawrence Carin
Findings of EMNLP (2021) (to appear)


It has been shown that training multi-task models with auxiliary tasks can improve the target tasks 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 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.

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