  Many sequence-to-sequence generation tasks, including machine translation and
text-to-speech, can be posed as estimating the density of the output y given
the input x: p(y|x). Given this interpretation, it is natural to evaluate
sequence-to-sequence models using conditional log-likelihood on a test set.
However, the goal of sequence-to-sequence generation (or structured prediction)
is to find the best output y^ given an input x, and each task has its own
downstream metric R that scores a model output by comparing against a set of
references y*: R(y^, y* | x). While we hope that a model that excels in density
estimation also performs well on the downstream metric, the exact correlation
has not been studied for sequence generation tasks. In this paper, by comparing
several density estimators on five machine translation tasks, we find that the
correlation between rankings of models based on log-likelihood and BLEU varies
significantly depending on the range of the model families being compared.
First, log-likelihood is highly correlated with BLEU when we consider models
within the same family (e.g. autoregressive models, or latent variable models
with the same parameterization of the prior). However, we observe no
correlation between rankings of models across different families: (1) among
non-autoregressive latent variable models, a flexible prior distribution is
better at density estimation but gives worse generation quality than a simple
prior, and (2) autoregressive models offer the best translation performance
overall, while latent variable models with a normalizing flow prior give the
highest held-out log-likelihood across all datasets. Therefore, we recommend
using a simple prior for the latent variable non-autoregressive model when fast
generation speed is desired.

Machine Learning;Machine Learning