  We conduct a large-scale fine-grained comparative analysis of machine
translations (MT) against human translations (HT) through the lens of
morphosyntactic divergence. Across three language pairs and two types of
divergence defined as the structural difference between the source and the
target, MT is consistently more conservative than HT, with less morphosyntactic
diversity, more convergent patterns, and more one-to-one alignments. Through
analysis on different decoding algorithms, we attribute this discrepancy to the
use of beam search that biases MT towards more convergent patterns. This bias
is most amplified when the convergent pattern appears around 50% of the time in
training data. Lastly, we show that for a majority of morphosyntactic
divergences, their presence in HT is correlated with decreased MT performance,
presenting a greater challenge for MT systems.
