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Quite surprisingly, exact maximum a posteriori (MAP) decoding of neurallanguage generators frequently leads to low-quality results. Rather, moststate-of-the-art results on language generation tasks are attained using beamsearch despite its overwhelmingly high search error rate. This implies that theMAP objective alone does not express the properties we desire in text, whichmerits the question: if beam search is the answer, what was the question? Weframe beam search as the exact solution to a different decoding objective inorder to gain insights into why high probability under a model alone may notindicate adequacy. We find that beam search enforces uniform informationdensity in text, a property motivated by cognitive science. We suggest a set ofdecoding objectives that explicitly enforce this property and find that exactdecoding with these objectives alleviates the problems encountered whendecoding poorly calibrated language generation models. Additionally, we analyzethe text produced using various decoding strategies and see that, in our neuralmachine translation experiments, the extent to which this property is adheredto strongly correlates with BLEU.

If beam search is the answer, what was the question?