  Multi-task learning (MTL) has achieved success over a wide range of problems,
where the goal is to improve the performance of a primary task using a set of
relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks
w.r.t. the primary task is not known a priori, the success of MTL models
depends on the correct choice of these auxiliary tasks and also a balanced
mixing ratio of these tasks during alternate training. These two problems could
be resolved via manual intuition or hyper-parameter tuning over all
combinatorial task choices, but this introduces inductive bias or is not
scalable when the number of candidate auxiliary tasks is very large. To address
these issues, we present AutoSeM, a two-stage MTL pipeline, where the first
stage automatically selects the most useful auxiliary tasks via a
Beta-Bernoulli multi-armed bandit with Thompson Sampling, and the second stage
learns the training mixing ratio of these selected auxiliary tasks via a
Gaussian Process based Bayesian optimization framework. We conduct several MTL
experiments on the GLUE language understanding tasks, and show that our AutoSeM
framework can successfully find relevant auxiliary tasks and automatically
learn their mixing ratio, achieving significant performance boosts on several
primary tasks. Finally, we present ablations for each stage of AutoSeM and
analyze the learned auxiliary task choices.
