In structured output prediction tasks, labeling ground-truth training output
is often expensive. However, for many tasks, even when the true output is
unknown, we can evaluate predictions using a scalar reward function, which may
be easily assembled from human knowledge or non-differentiable pipelines. But
searching through the entire output space to find the best output with respect
to this reward function is typically intractable. In this paper, we instead use
efficient truncated randomized search in this reward function to train
structured prediction energy networks (SPENs), which provide efficient
test-time inference using gradient-based search on a smooth, learned
representation of the score landscape, and have previously yielded
state-of-the-art results in structured prediction. In particular, this
truncated randomized search in the reward function yields previously unknown
local improvements, providing effective supervision to SPENs, avoiding their
traditional need for labeled training data.

文章介绍了一种使用截断随机搜索来训练结构化预测能量网络（SPENs）的方法，以达到在无标记训练数据的情况下有效监督和提高性能的目的。通过使用简单易得的人类知识或不可微分的流水线来组装标量奖励函数，并使用快速的截断随机搜索来有效地搜索输出空间产生局部优化，以避免使用标记训练数据。SPENs 提供了高效的测试时间推理，并在结构化预测中产生先进的结果。