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.

该研究对语言生成任务和解码策略之间的相互作用进行了全面分析，测量了生成文本属性随着解码策略和任务的变化情况，并使用人工和自动评估发现了之前观察到的和令人惊讶的结果，如语言生成中的多样性与质量之间的平衡是非常特定于任务的，而模式搜索方法如光束搜索在机器翻译中表现出色，但在故事生成中会导致不连贯和重复的文本。