  We inspect the multi-head self-attention in Transformer NMT encoders for
three source languages, looking for patterns that could have a syntactic
interpretation. In many of the attention heads, we frequently find sequences of
consecutive states attending to the same position, which resemble syntactic
phrases. We propose a transparent deterministic method of quantifying the
amount of syntactic information present in the self-attentions, based on
automatically building and evaluating phrase-structure trees from the
phrase-like sequences. We compare the resulting trees to existing constituency
treebanks, both manually and by computing precision and recall.
