Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Scheduling for Multi-Task Learning   To train neural machine translation models simultaneously on multiple tasks
(languages), it is common to sample each task uniformly or in proportion to
dataset sizes. As these methods offer little control over performance
trade-offs, we explore different task scheduling approaches. We first consider
existing non-adaptive techniques, then move on to adaptive schedules that
over-sample tasks with poorer results compared to their respective baseline. As
explicit schedules can be inefficient, especially if one task is highly
over-sampled, we also consider implicit schedules, learning to scale learning
rates or gradients of individual tasks instead. These techniques allow training
multilingual models that perform better for low-resource language pairs (tasks
with small amount of data), while minimizing negative effects on high-resource
tasks.

10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys