# Is optimizing weighted sum multi objective tasks considered a multi-task learning?

I have two sequence prediction tasks, finding $$\vec{\pi} \in \Pi$$ and $$\vec{\psi} \in \Psi$$. Each sequence has its own objective function, i.e. $$f_1(\vec{\pi})$$ and $$f_2(\vec{\psi})$$. The input for the two sequence prediction tasks are also of different domain.

Say that by modification and extension in the model design, I can use one seq2seq or Pointer Network (or its variants) to produce the two sequence one at a time. In the training stage, however, the two objective functions are combined into $$F(\vec{\pi}, \vec{\psi}) = \alpha f_1(\vec{\pi}) + \beta f_2(\vec{\psi})$$ and the loss function to train the model use the combined objective function $$F(\vec{\pi}, \vec{\psi})$$.