  When generating text from probabilistic models, the chosen decoding strategy
has a profound effect on the resulting text. Yet the properties elicited by
various decoding strategies do not always transfer across natural language
generation tasks. For example, while mode-seeking methods like beam search
perform remarkably well for machine translation, they have been observed to
lead to incoherent and repetitive text in story generation. Despite such
observations, the effectiveness of decoding strategies is often assessed with
respect to only a single task. This work -- in contrast -- provides a
comprehensive analysis of the interaction between language generation tasks and
decoding strategies. Specifically, we measure changes in attributes of
generated text as a function of both decoding strategy and task using human and
automatic evaluation. Our results reveal both previously-observed and
surprising findings. For example, the nature of the diversity-quality trade-off
in language generation is very task-specific; the length bias often attributed
to beam search is not constant across tasks.
