Our main contribution is to provide several novel ways to deconstruct supertags to create multiple alternative auxiliary tasks, which we then combine using a multi-task prediction framework and we show that this can lead to a significant improvement in supertagging accuracy.
we found in our experiments that the results were not very sensitive to the values, and the uniform distribution over all the tasks performed the best.