  Teaching is critical to human society: it is with teaching that prospective
students are educated and human civilization can be inherited and advanced. A
good teacher not only provides his/her students with qualified teaching
materials (e.g., textbooks), but also sets up appropriate learning objectives
(e.g., course projects and exams) considering different situations of a
student. When it comes to artificial intelligence, treating machine learning
models as students, the loss functions that are optimized act as perfect
counterparts of the learning objective set by the teacher. In this work, we
explore the possibility of imitating human teaching behaviors by dynamically
and automatically outputting appropriate loss functions to train machine
learning models. Different from typical learning settings in which the loss
function of a machine learning model is predefined and fixed, in our framework,
the loss function of a machine learning model (we call it student) is defined
by another machine learning model (we call it teacher). The ultimate goal of
teacher model is cultivating the student to have better performance measured on
development dataset. Towards that end, similar to human teaching, the teacher,
a parametric model, dynamically outputs different loss functions that will be
used and optimized by its student model at different training stages. We
develop an efficient learning method for the teacher model that makes gradient
based optimization possible, exempt of the ineffective solutions such as policy
optimization. We name our method as "learning to teach with dynamic loss
functions" (L2T-DLF for short). Extensive experiments on real world tasks
including image classification and neural machine translation demonstrate that
our method significantly improves the quality of various student models.
