  Sequence learning has attracted much research attention from the machine
learning community in recent years. In many applications, a sequence learning
task is usually associated with multiple temporally correlated auxiliary tasks,
which are different in terms of how much input information to use or which
future step to predict. For example, (i) in simultaneous machine translation,
one can conduct translation under different latency (i.e., how many input words
to read/wait before translation); (ii) in stock trend forecasting, one can
predict the price of a stock in different future days (e.g., tomorrow, the day
after tomorrow). While it is clear that those temporally correlated tasks can
help each other, there is a very limited exploration on how to better leverage
multiple auxiliary tasks to boost the performance of the main task. In this
work, we introduce a learnable scheduler to sequence learning, which can
adaptively select auxiliary tasks for training depending on the model status
and the current training data. The scheduler and the model for the main task
are jointly trained through bi-level optimization. Experiments show that our
method significantly improves the performance of simultaneous machine
translation and stock trend forecasting.

Computation and Language