Generative Bridging Network in Neural Sequence Prediction

28 Jun 2017 Wenhu Chen Guanlin Li Shuo Ren Shujie Liu Zhirui Zhang Mu Li Ming Zhou

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... (read more)

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