First published: 2016/09/01 (6 years ago) Abstract: A key problem in structured output prediction is direct optimization of the
task reward function that matters for test evaluation. This paper presents a
simple and computationally efficient approach to incorporate task reward into a
maximum likelihood framework. We establish a connection between the
log-likelihood and regularized expected reward objectives, showing that at a
zero temperature, they are approximately equivalent in the vicinity of the
optimal solution. We show that optimal regularized expected reward is achieved
when the conditional distribution of the outputs given the inputs is
proportional to their exponentiated (temperature adjusted) rewards. Based on
this observation, we optimize conditional log-probability of edited outputs
that are sampled proportionally to their scaled exponentiated reward. We apply
this framework to optimize edit distance in the output label space. Experiments
on speech recognition and machine translation for neural sequence to sequence
models show notable improvements over a maximum likelihood baseline by using
edit distance augmented maximum likelihood.