  Solid evaluation of neural machine translation (NMT) is key to its
understanding and improvement. Current evaluation of an NMT system is usually
built upon a heuristic decoding algorithm (e.g., beam search) and an evaluation
metric assessing similarity between the translation and golden reference.
However, this system-level evaluation framework is limited by evaluating only
one best hypothesis and search errors brought by heuristic decoding algorithms.
To better understand NMT models, we propose a novel evaluation protocol, which
defines model errors with model's ranking capability over hypothesis space. To
tackle the problem of exponentially large space, we propose two approximation
methods, top region evaluation along with an exact top-$k$ decoding algorithm,
which finds top-ranked hypotheses in the whole hypothesis space, and Monte
Carlo sampling evaluation, which simulates hypothesis space from a broader
perspective. To quantify errors, we define our NMT model errors by measuring
distance between the hypothesis array ranked by the model and the ideally
ranked hypothesis array. After confirming the strong correlation with human
judgment, we apply our evaluation to various NMT benchmarks and model
architectures. We show that the state-of-the-art Transformer models face
serious ranking issues and only perform at the random chance level in the top
region. We further analyze model errors on architectures with different depths
and widths, as well as different data-augmentation techniques, showing how
these factors affect model errors. Finally, we connect model errors with the
search algorithms and provide interesting findings of beam search inductive
bias and correlation with Minimum Bayes Risk (MBR) decoding.
