  Despite major advances in open-ended text generation, there has been limited
progress in designing evaluation metrics for this task. We propose MAUVE -- a
metric for open-ended text generation, which directly compares the distribution
of machine-generated text to that of human language. MAUVE measures the mean
area under the divergence curve for the two distributions, exploring the
trade-off between two types of errors: those arising from parts of the human
distribution that the model distribution approximates well, and those it does
not. We present experiments across two open-ended generation tasks in the web
text domain and the story domain, and a variety of decoding algorithms and
model sizes. Our results show that evaluation under MAUVE indeed reflects the
more natural behavior with respect to model size, compared to prior metrics.
MAUVE's ordering of the decoding algorithms also agrees with that of generation
perplexity, the most widely used metric in open-ended text generation; however,
MAUVE presents a more principled evaluation metric for the task as it considers
both model and human text.
