A generalized decoding method for neural text generation.

In natural language generation, most decoding methods are not intrinsic because their performance depends on extrinsically configured hyperparameters. It means that: first, the generation system is dynamic under different conditions while the decoding system is always static under any conditions once its hyperparameters are extrinsically fixed; second, it is hard to select a constant decoding hyperparameter that is omnipotent for all conditions. Although there are decoding methods that are hyperparameter-free, such as greedy and plain sampling, it has been well studied that these methods generally perform worse than methods with hyperparameters, such as beam search, top-k and top-p. Decoding with hyperparameters can get infinite strategies from different fixed configurations, while hyperparameter-free methods have only one strategy. Therefore, the comparison between them is actually unfair, which is a one-vs-infinite battle. So how to deal with the decoding hyperparameters properly and intrinsically? Is it true that hyperparameter-free methods are always inferior to methods with inexhaustible hyperparameter configurations? Is it possible to design a generalized framework, by which these decoding methods can be naturally connected, uniformly described, and mutually inspired? In these paper, we try to find answers to these questions.