  We propose a method for natural language generation, choosing the most
representative output rather than the most likely output. By viewing the
language generation process from the voting theory perspective, we define
representativeness using range voting and a similarity measure. The proposed
method can be applied when generating from any probabilistic language model,
including n-gram models and neural network models. We evaluate different
similarity measures on an image captioning task and a machine translation task,
and show that our method generates longer and more diverse sentences, providing
a solution to the common problem of short outputs being preferred over longer
and more informative ones. The generated sentences obtain higher BLEU scores,
particularly when the beam size is large. We also perform a human evaluation on
both tasks and find that the outputs generated using our method are rated
higher.
