word “different” strongly affects the occurrences
of those words before it. These strong inﬂuences
are shown by the darker-colored pixels seen in the
second last column of the impact map. This ob-
servation agrees with the ground-truth dependency
tree, which selects “ different” as the head of all
remaining words in the phrase “ this will be a lit-
tle different.” We also observe similar patterns on
“transitions” and “Hill”. Such correlations lead us
to explore the idea of extracting dependency trees
from the matrices (see Section 4.1).
follow social media transitions on Capitol Hill
Figure 2: Part of the constituency tree.
Constituency. Figure 2 shows part of the con-
stituency tree of our example sentence generated
by Stanford CoreNLP ( Manning et al. , 2014). In
this sentence, “ media” and “ on” are two words
that are adjacent to “ transitions”. From the tree,
however, we see that “media” is closer to “transi-
tions” than “on” is in terms of syntactic distance.
If a model is syntactically uninformed, we would
expect “media” and “on” to have comparable im-
pacts on the prediction of “ transitions”, and vice
versa. However, we observe a far greater impact
(darker color) between “media” and “transitions”
than that between “on” and “transitions”. We will
further support this observation with empirical ex-
periments in Section 4.2.
Other Structures. Along the diagonal of the
impact map, we see that words are grouped into
four contiguous chunks that have speciﬁc intents
(e.g., a noun phrase – on Capitol Hill ). We also
observe that the two middle chunks have relatively
strong inter-chunk word impacts and thus a bond-
ing that groups them together, forming a larger
verb phrase. This observation suggest that BERT
may capture the compositionality of the language.
In the following sections we quantitatively eval-
uate these observations.
4 Syntactic Probe
We start with two syntactic probes – dependency
probe and constituency probe.
4.1 Dependency Probe
With the goal of exploring the extent dependency
relations are captured in BERT, we set out to an-
swer the following question: Can BERT outper-
form linguistically uninformed baselines in unsu-
pervised dependency parsing? If so, to what ex-
tent?
We begin by using the token-level perturbed
masking technique to extract an impact matrix F
for each sentence. We then utilize graph-based al-
gorithms to induce a dependency tree fromF, and
compare it against ground-truth whose annotations
Figure 1: Parameter-free probe for syntactic knowledge:
words sharing syntactic subtrees have larger impact on
each other in the MLM prediction (Wu et al., 2020)
parameter-free approach based on measuring the
impact that one word has on predicting another
word within a sequence in the MLM task (Figure 1).
They concluded that BERT "naturally" learns
some syntactic information, although it is not
very similar to linguistic annotated resources.
The ﬁll-in-the-gap probes of MLM showed that
BERT takes subject-predicate agreement into
account when performing the cloze task (Gold-
berg, 2019; van Schijndel et al., 2019), even for
meaningless sentences and sentences with distrac-
tor clauses between the subject and the verb (Gold-
berg, 2019). A study of negative polarity items
(NPIs) by Warstadt et al. (2019) showed thatBERT
is better able to detect the presence of NPIs(e.g.
"ever") and the words that allow their use (e.g.
"whether") than scope violations.
The above claims of syntactic knowledge are be-
lied by the evidence that BERT does not "under-
stand" negation and is insensitive to malformed
input. In particular, its predictions were not al-
tered2 even with shufﬂed word order, truncated
sentences, removed subjects and objects (Ettinger,
2019). This could mean that either BERT’s syn-
tactic knowledge is incomplete, or it does not
need to rely on it for solving its tasks. The latter
seems more likely, since Glavaš and Vuli´c (2020)
2See also the recent ﬁndings on adversarial triggers, which