  In order to alleviate data sparsity and overfitting problems in maximum
likelihood estimation (MLE) for sequence prediction tasks, we propose the
Generative Bridging Network (GBN), in which a novel bridge module is introduced
to assist the training of the sequence prediction model (the generator
network). Unlike MLE directly maximizing the conditional likelihood, the bridge
extends the point-wise ground truth to a bridge distribution conditioned on it,
and the generator is optimized to minimize their KL-divergence. Three different
GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to
penalize confidence, enhance language smoothness and relieve learning burden.
Experiments conducted on two recognized sequence prediction tasks (machine
translation and abstractive text summarization) show that our proposed GBNs can
yield significant improvements over strong baselines. Furthermore, by analyzing
samples drawn from different bridges, expected influences on the generator are
verified.
