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Probing for Predicate Argument Structures in Pretrained Language Models

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Abstract

Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency-and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL). These results have prompted researchers to investigate the inner workings of modern PLMs with the aim of understanding how, where, and to what extent they encode information about SRL. In this paper, we follow this line of research and probe for predicate argument structures in PLMs. Our study shows that PLMs do encode semantic structures directly into the con-textualized representation of a predicate, and also provides insights into the correlation between predicate senses and their structures, the degree of transferability between nominal and verbal structures, and how such structures are encoded across languages. Finally, we look at the practical implications of such insights and demonstrate the benefits of embedding predicate argument structure information into an SRL model.
Probing for Predicate Argument Structures
in Pretrained Language Models
Simone Conia1and Roberto Navigli2
Sapienza NLP Group
1Department of Computer Science
2Department of Computer, Control and Management Engineering
Sapienza University of Rome
conia@di.uniroma1.it navigli@diag.uniroma1.it
Abstract
Thanks to the effectiveness and wide avail-
ability of modern pretrained language models
(PLMs), recently proposed approaches have
achieved remarkable results in dependency-
and span-based, multilingual and cross-lingual
Semantic Role Labeling (SRL). These results
have prompted researchers to investigate the
inner workings of modern PLMs with the aim
of understanding how, where, and to what ex-
tent they encode information about SRL. In
this paper, we follow this line of research
and probe for predicate argument structures
in PLMs. Our study shows that PLMs do en-
code semantic structures directly into the con-
textualized representation of a predicate, and
also provides insights into the correlation be-
tween predicate senses and their structures, the
degree of transferability between nominal and
verbal structures, and how such structures are
encoded across languages. Finally, we look at
the practical implications of such insights and
demonstrate the benefits of embedding pred-
icate argument structure information into an
SRL model.
1 Introduction
Semantic Role Labeling (
SRL
) is often defined in-
formally as the task of automatically answering the
question “Who did What to Whom, Where, When
and How?” (Màrquez et al.,2008) and is, there-
fore, thought to be a fundamental step towards
Natural Language Understanding (Navigli,2018).
Over the past few years,
SRL
has started to gain
renewed traction, thanks mainly to the effective-
ness and wide availability of modern pretrained
language models (
PLM
s), such as ELMo (Peters
et al.,2018), BERT (Devlin et al.,2019) and BART
(Lewis et al.,2020). Current approaches have, in-
deed, attained impressive results on standard evalu-
ation benchmarks for dependency- and span-based,
multilingual and cross-lingual
SRL
(He et al.,2019;
Li et al.,2019;Cai and Lapata,2020;Conia and
Navigli,2020;Blloshmi et al.,2021;Conia et al.,
2021).
Despite the remarkable benefits provided by the
rich contextualized word representations coming
from
PLM
s, the novelties introduced in recent state-
of-the-art models for
SRL
revolve primarily around
developing complexities on top of such word repre-
sentations, rather than investigating what happens
inside a
PLM
. For example, the
SRL
systems of
He et al. (2019) and Conia and Navigli (2020) take
advantage only of BERT’s uppermost hidden layers
to build their input word representations. However,
the revolution that
PLM
s have sparked in numer-
ous areas of Natural Language Processing (
NLP
)
has motivated researchers in the community to in-
vestigate the inner workings of such models, with
the aim of understanding how, where, and to what
extent they encode information about specific tasks.
This research has revealed that different layers en-
code significantly different features (Tenney et al.,
2019;Vuli´
c et al.,2020). In perhaps one of the
most notable studies in this direction, Tenney et al.
(2019) demonstrated empirically that BERT “re-
discovers” the classical
NLP
pipeline, highlighting
that the lower layers tend to encode mostly lexical-
level information while upper layers seem to favor
sentence-level information.
Although recent analyses have already provided
important insights into which layers of a
PLM
are
more relevant for
SRL
and how their relative im-
portance is affected by the linguistic formalism
of choice (Kuznetsov and Gurevych,2020), not
only do these analyses treat
SRL
as an atomic task
but they also do not explore taking advantage of
their insights to improve current state-of-the-art
SRL
systems. Indeed, the
SRL
pipeline is usually
divided into four main steps: predicate identifica-
tion and disambiguation, and argument identifica-
tion and classification. To address this gap, in this
paper we therefore take an in-depth look at how
predicate senses and their predicate argument struc-
tures (
PAS
s) are encoded across different layers of
different
PLM
s. On the one hand, we provide new
insights into the capability of these models to cap-
ture complex linguistic features, while on the other,
we show the benefits of embedding such features
into SRL systems to improve their performance.
Our contributions can be summarized as follows:
We probe
PLM
s for
PAS
s: do
PLM
s encode
the argument structure of a predicate in its
contextual representation?
We show that, even though a
PAS
is defined
according to a predicate sense, senses and ar-
gument structures are encoded at different lay-
ers in PLMs;
We demonstrate empirically that verbal and
nominal
PAS
s are represented differently
across the layers of a PLM;
Current
SRL
systems do not discriminate be-
tween nominal and verbal
PAS
s: we demon-
strate that, although there exists some degree
of transferability between the two, an
SRL
system benefits from treating them separately;
We find that
PAS
information is encoded sim-
ilarly across two very different languages, En-
glish and Chinese, in multilingual PLMs;
We corroborate our findings by proposing
a simple approach for integrating predicate-
argument structure knowledge into an
SRL
ar-
chitecture, attaining improved results on stan-
dard gold benchmarks.
We hope that our work will contribute both to the
understanding of the inner workings of modern
pretrained language models and to the development
of more effective
SRL
systems. We release our
software for research purposes at
https://github.
com/SapienzaNLP/srl-pas- probing.
2 Related Work
Probing pretrained language models.
The un-
precedented capability of modern
PLM
s to provide
rich contextualized input representations took the
NLP
community by storm. Alongside the rising
wave of successes collected by
PLM
s in an ever
increasing number of areas, researchers started to
question and investigate what happens inside these
models and what they really capture, probing for
knowledge and linguistic properties (Hewitt and
Manning,2019;Chi et al.,2020;Vuli´
c et al.,2020).
This body of work quickly attracted increasing at-
tention and grew to become a field of study with a
name of its own: BERTology (Rogers et al.,2020).
Probing a
PLM
usually consists in defining a very
precise task (e.g., identifying whether two words
are linked by a syntactic or semantic relation), and
then in designing and training a simple model,
called a probe, to solve the task using the con-
textualized representations provided by the
PLM
.
The idea is to design a probe that is as simple as
possible, often consisting of a single-layer model:
if the probe is able to address the task, then it must
be thanks to the contextual information captured by
the
PLM
as the expressiveness of the probe itself
is limited by its simplicity. One could argue that
some complex relations may require a non-linear
probe (White et al.,2021) which can reveal hidden
information as long as it is accompanied by control
experiments (Hewitt and Liang,2019) to verify that
it is still extracting information from the underly-
ing
PLM
, rather than merely learning to solve the
probing task. Over the past few years, these prob-
ing techniques have been used to great effect and
revealed that
PLM
s have been “rediscovering” the
classical
NLP
pipeline (Tenney et al.,2019), and
that they often encode distances between syntactic
constituents (Hewitt and Liang,2019), lexical re-
lations (Vuli´
c et al.,2020) and morphology (Chi
et al.,2020), inter alia.
Probing techniques for SRL.
As in several
other fields of
NLP
, recent studies have aimed to
shed some light on how, where and to what ex-
tent
PLM
s encode information relevant to
SRL
.
Among others, Tenney et al. (2019) devised an
edge probing mechanism aimed at ascertaining
the capability of BERT to identify which seman-
tic role ties a given predicate to a given argument
span, and showed that this task is “solved” mainly
by the middle layers of BERT. Toshniwal et al.
(2020) proposed and compared several techniques
for better combining the contextualized representa-
tions of a
PLM
, finding that applying max pooling
or performing a weighted average are two robust
strategies for
SRL
. More recently, Kuznetsov and
Gurevych (2020) designed a probe to analyze how
different linguistic ontologies – essential to the task
in that they define predicate senses and semantic
roles explicitly – require features that are encoded
at different layers of a
PLM
. In this paper, we
follow the line of research laid out by the afore-
mentioned work, probing
PLM
s with the objective
of understanding where and to what extent they
encode a predicate argument structure into the con-
textualized representation of a predicate.
Recent advances in SRL.
Thanks to their effec-
tiveness,
PLM
s are now the de facto input rep-
resentation method in
SRL
(He et al.,2019;Li
et al.,2019;Conia and Navigli,2020;Blloshmi
et al.,2021). Recently proposed approaches have
achieved impressive results on several gold bench-
marks (Hajiˇ
c et al.,2009;Pradhan et al.,2012),
both in span-based and in dependency-based
SRL
,
but also in multilingual and cross-lingual
SRL
,
even though there still seems to be a significant
margin for improvement in out-of-domain settings.
The innovations put forward by such approaches,
however, have mainly focused on architectural nov-
elties built on top of
PLM
s: Cai et al. (2018) pro-
posed the first end-to-end architecture; He et al.
(2019) and Cai and Lapata (2019) successfully ex-
ploited syntax in multilingual
SRL
;Marcheggiani
and Titov (2020) took advantage of GCNs to cap-
ture distant semantic relations; Conia and Navigli
(2020) devised a language-agnostic approach to
bridge the gap in multilingual
SRL
;Blloshmi et al.
(2021) and Paolini et al. (2021) tackled the task as
a sequence generation problem; Conia et al. (2021)
introduced a model to perform cross-lingual
SRL
across heterogeneous linguistic inventories. How-
ever, if we look back at past work, it is easy to
realize that we lack a study that provides an in-
depth look into
PLM
s and a hint at how to better
exploit them in future SRL systems.
3 Probing for Predicate Senses and Their
Predicate-Argument Structures
As mentioned above, some studies have already
investigated how semantic knowledge is distributed
among the inner layers of current
PLM
s, finding
that information useful for
SRL
is mainly stored in
their middle layers (Tenney et al.,2019). However,
such studies have considered
SRL
as an atomic
task, while instead the
SRL
pipeline can be thought
of as being composed of four different subtasks:
1. Predicate identification
, which consists in
identifying all those words or multi-word ex-
pressions that denote an action or an event in
the input sentence;
2. Predicate sense disambiguation
, which re-
quires choosing the most appropriate sense
or frame for each predicate identified, as the
same predicate may denote different meanings
or define different semantic scenarios depend-
ing on the context;
3. Argument identification
, which consists in
selecting the parts of the input text that are
“semantically” linked as arguments to an iden-
tified and disambiguated predicate;
4. Argument classification
, which is the task of
determining which kind of semantic relation,
i.e., semantic role, governs each predicate-
argument pair.
For our study, it is important to note that, in many
popular ontologies for
SRL
, predicate senses or
frames are often tightly coupled to their possible
semantic roles. In other words, the set of possi-
ble semantic roles that can be linked to a predicate
p
is defined according to the sense or frame of
p
.
Hereafter, given a predicate
p
, we refer to its set
of possible semantic roles as the roleset of
p
. For
example, the predicate love as in “He loved every-
thing about her” belongs to the FrameNet (Baker
et al.,1998) frame experiencer_focused_emotion
which defines a roleset composed of {Experiencer,
Content,
. . .
, Degree}. The same predicate sense
has different rolesets in other ontologies, for exam-
ple {ARG0 (lover), ARG1 (loved)} in the English
PropBank (Palmer et al.,2005) and {Experiencer,
Stimulus,
. . .
, Cause} in VerbAtlas (Di Fabio et al.,
2019).
3.1 Predicate Senses and Their Rolesets
Since rolesets are often defined according to predi-
cate senses, it is interesting to investigate whether
current pretrained language models store important
features about senses and rolesets in their hidden
layers. To this end, we formulate two simple prob-
ing tasks:
Sense probing
, which consists in predicting
the sense
s
of a predicate
p
from the contex-
tual vector representation
xp
of
p
, where
xp
is obtained from a pretrained language model.
Roleset probing
, which consists in predicting
the semantic roles
{r1, r2, . . . , rn}
that appear
linked to a predicate
p
from its contextual
representation
xp
, where
xp
is obtained from
a pretrained language model.
For the choice of
xp
, we compare four different
options:
Random:
initializing the weights of the lan-
guage model at random provides a simple con-
trol baseline to attest the ability of a probe to
“learn the probing task”, i.e. learning to asso-
ciate random inputs to correct labels;
Static: xp
is the input embedding of the pre-
trained language model corresponding to
p
,
e.g., the non-contextual representation before
the Transformer layers in BERT.1
Top-4: xp
is the concatenation of the topmost
four hidden layers of the language model: this
is the configuration used in some of the re-
cently proposed approaches for full
SRL
sys-
tems (Conia and Navigli,2020);
W-Avg: xp
is the weighted average of all the
hidden layers of the language model, where
the weights for each layer are learned during
training (the larger the weight the more impor-
tant its corresponding layer is for the probing
task).
For each probing task, we train
2
two simple probes,
a linear classifier and a non-linear
3
classifier, on
the verbal predicate instances of the English train-
ing datasets provided as part of the CoNLL-2009
shared task for dependency-based
SRL
(Hajiˇ
c et al.,
2009).
3.2 Probing Results
Results on sense probing.
Table 1reports the
results of our linear and non-linear probes on pred-
icate sense disambiguation when using different
types of input representations
xp
, namely, Static,
Random, Last-4 and W-Avg, of an input predicate
p
in context. The Random baseline is able to dis-
ambiguate well (84.8% in Accuracy using BERT-
base-cased), which is, however, unsurprising since
CoNLL-2009 is tagged with PropBank labels and
most of the predicates are annotated with their first
sense (e.g., buy.01,sell.01). Interestingly, static
representations from all four language models do
1
In case of a predicate composed of multiple subtokens,
xp
is the average of the vector representations of its subtokens.
2
We train each probe for 20 epochs using Adam (Kingma
and Ba,2015) as the optimizer with a learning rate of 1e-3. As
is customary in probing studies, the weights of the pretrained
language models are kept frozen during training. We use the
pretrained language models made available by Huggingface’s
Transformers library (Wolf et al.,2020).
3
We use the Swish activation function (Ramachandran
et al.,2018) for our non-linear probes.
BERT RoBERTa m-BERT XLM-R
Linear
Random 84.8 85.6
Static 84.7 86.6
Top-4 92.8 93.4
W-Avg 94.4 94.5 – –
Non-Linear
Random 84.3 83.6 83.7 84.2
Static 86.4 86.6 86.1 86.1
Top-4 93.2 93.6 92.3 93.3
W-Avg 94.2 94.8 93.4 94.2
Table 1: Results on sense probing in terms of Ac-
curacy (%) for the Random, Static, Top-4 and W-
Avg probes using different pretrained language models,
namely, BERT (base-cased), RoBERTa (base), multi-
lingual BERT (base) and XLM-RoBERTa (base). Us-
ing a weighted average of all the hidden layers is a bet-
ter choice than using the concatenation of the topmost
four layers as in Conia and Navigli (2020).
not contain much more information about predi-
cate senses than random representations. Using
the topmost four hidden layers, instead, provides a
substantial improvement over static representations
for all language models (e.g., +6% in Accuracy for
BERT-base-cased), lending credibility to the fact
that context is key for the disambiguation process.
Most notably, the best representation for the sense
probing task is consistently obtained by perform-
ing a weighted average of all the hidden layers of
the language model. This shows that important
predicate sense information is not stored only in
the topmost hidden layers and, therefore, also hints
at the possibility that state-of-the-art architectures,
such as those of He et al. (2019) and Conia and
Navigli (2020), do not exploit pretrained language
models to their fullest. Finally, it is interesting to
note that linear and non-linear probes obtain similar
results, showing that sense-related information can
easily be extracted without the need for a complex
probe.
Results on roleset probing.
Table 2reports the
results on roleset identification obtained by our
linear and non-linear probes when using different
types of input representations
xp
, namely, Static,
Random, Top-4 and W-Avg, of an input predicate
p
in context. For this task, we measure the per-
formance of a probe in terms of micro-averaged
F1 score, taking into account partially correct pre-
dictions, e.g., the system is partially rewarded for
predicting {ARG0, ARG1} instead of {ARG0,
ARG2}. As is the case for sense probing, our sim-
ple Random baseline is able to identify the correct
roleset for a predicate in context with a satisfactory
performance (72.8% in F1 score using BERT-base-
cased). Indeed, most predicates have at least one ar-
gument tagged with either ARG0 or ARG1, which
in PropBank usually correspond to agentive and pa-
tientive proto-roles, respectively; we hypothesize
that the Random probe merely learns to bias its
predictions towards these very common semantic
roles. Differently from in the sense probing task,
the non-linear probe seems to perform better and
achieve higher scores than the linear one. However,
this does not mean that roleset-related features are
“stored” non-linearly in
PLM
s. Indeed, one can no-
tice that the random non-linear probe also performs
better than its linear counterpart, suggesting that
the higher score is due to the greater expressiveness
of the probe, which “learns” the task rather than
“extracting” information from the underlying
PLM
,
i.e., the selectivity (Hewitt and Liang,2019) of a
non-linear probe is not greater than that of a linear
probe in this task.
Despite the fact that the roleset probing task
is more difficult than the sense probing one, we
can observe a similar trend in the results: the
Top-4 probe is substantially better than the Static
probe, but W-Avg consistently outperforms Top-
4, strongly suggesting that future approaches will
need to use all the layers to take full advantage of
the knowledge encoded within
PLM
s. We stress
that not exploiting all the inner layers of a
PLM
is
an illogical choice, since the cost of computing a
weighted average of their hidden representations is
negligible compared to the overall computational
cost of a Transformer-based architecture.
On the correlation between senses and rolesets.
Thus far, we have seen empirical evidence that
PLM
s encode important features about predicate
senses and their rolesets across all their hidden
layers, not just the topmost ones often used in the
literature by current models for
SRL
. However,
one may wonder how such features are distributed
across these hidden layers. As we have already
discussed above, predicate senses and their rolesets
are tightly coupled: do
PLM
s distribute sense and
roleset features similarly over their inner layers?
To answer this question, we resort to the W-Avg
probe we introduced above. Indeed, its peculiarity
is that it learns to assign a different weight to each
hidden layer of a
PLM
: in order to minimize the
training loss, the W-Avg probe will assign a larger
weight to those layers that are most beneficial, i.e.,
BERT RoBERTa m-BERT XLM-R
Linear
Random 72.8 72.8
Static 75.1 75.3
Top-4 85.3 85.3
W-Avg 85.7 86.1 – –
Non-Linear
Random 75.9 75.9 75.8 75.7
Static 76.3 76.5 76.2 76.3
Top-4 89.2 88.8 88.0 88.9
W-Avg 89.4 89.3 88.8 89.1
Table 2: Results on roleset probing in terms of F1
Score (%) for the Random, Static, Top-4 and W-
Avg probes using different pretrained language models,
namely, BERT (base-cased), RoBERTa (base), multi-
lingual BERT (base) and XLM-RoBERTa (base). As
for the sense probing task, using the a weighted aver-
age of all the hidden layers provides richer features to
the probes.
to those layers that express features that are more
relevant for the probing task. Therefore, we extract
such layer weights learned by our probes for the
two tasks we are studying – predicate sense disam-
biguation and roleset identification – and compare
these learned weights, as shown in Figure 1(top,
blue charts). Interestingly, and perhaps surprisingly,
the W-Avg probe learns a different weight distribu-
tion for the two probing tasks, even though rolesets
are often defined on the basis of predicate senses in
many popular ontologies for
SRL
. We can observe
that predicate sense features are encoded more uni-
formly across the hidden layers of BERT or, equiv-
alently, that the probe assigns similar weights to
each hidden layer, slightly preferring the topmost
ones (Figure 1, top-left). However, this is not the
case for the roleset probing task, in which the probe
mostly relies on the hidden layers going from the
6th to the 10th, almost disregarding the bottom and
top ones. Furthermore, we can observe the same
negative correlation within the distributions of the
layer weights learned for senses and rolesets when
using RoBERTa, albeit the divergence is slightly
less accentuated (Figure 1, top-right).
3.3 Verbal and Nominal Predicates
One aspect that is often overlooked when designing
and proposing novel architectures for
SRL
is that
not all predicates are verbs. In English, it is easy to
find examples of nouns that evoke or imply a predi-
cation, such as producer,driver, and writer. Most
common nominal predicates are “verb-derived” or
“deverbal” as their roleset is derived from their cor-
responding verbal predicates. This is why, per-
2 4 6 8 10 12
0
5
10
15
20 Sense
Roleset
PLM layers
R o B E R T a
2 4 6 8 10 12
0
5
10
15
20
25
30
35 Sense
Roleset
PLM layers
B E R T
Relative importance (% )
2 4 6 8 10 12
0
2
4
6
8
10
12
14
PLM layers
Relative importance (% )
S ense
Roleset
S ense
Roleset
2 4 6 8 10 12
0
2
4
6
8
10
12
14
16
PLM layers
Relative importance (% )
Ve r b Pr ed ic a te sNo u n Pr ed ic a te s
Figure 1: Relative importance (%) of each layer of BERT (left) and RoBERTa (right) for sense probing and roleset
probing. Verbal predicates (top, blue): the most important layers of a PLM for roleset probing are the middle
layers, especially for BERT, in which the top and the bottom layers are almost completely discarded. Nominal
predicates (bottom, green): the importance of each layer follows the same trend for both sense and roleset probing.
PLM Trained on Verbs (F1) Nouns (F1)
Random Verbs 72.0
Random Nouns 68.5
BERT Verbs 85.7 63.3
BERT Nouns 67.5 77.5
RoBERTa Verbs 86.1 64.7
RoBERTa Nouns 67.5 78.3
Table 3: Results in terms of F1 score (%) on zero-shot
roleset identification when a probe is trained on ver-
bal predicates and evaluated on nominal predicates, and
vice versa. Interestingly, a probe trained on verbal pred-
icates performs worse than a random probe on nominal
predicates, demonstrating that knowledge transfer be-
tween predicate types is not trivial.
haps, current state-of-the-art approaches do not dis-
tinguish between verbal and nominal predicates.
4
However, nominal predicates also possess peculiar-
ities that do not appear in their verbal counterparts;
for example, a nominal predicate can be its own ar-
gument, e.g., writer is the agent itself of the action
4
We note that, in general, languages – English included
– also possess, sometimes in extensive quantities, predicates
that are neither verbal nor nominal. For example, Japanese
prominently features adjectival predicates.
write.
We take this opportunity to investigate how nom-
inal predicate senses and their rolesets are encoded
by
PLM
s in their inner layers. We train a W-Avg
probe on the sense and roleset probing tasks, fo-
cusing only on the nominal predicate instances
in CoNLL-2009. Figure 1(bottom, green charts)
shows the weights learned for the sense and roleset
probing tasks when using BERT (bottom-left) and
RoBERTa (bottom-right): we can immediately ob-
serve that, differently from verbal predicates, the
weight distributions learned for nominal senses and
their rolesets follow the same trend in both
PLM
s.
In other words, despite the fact that most nominal
predicates are verb-derived, their information is en-
coded dissimilarly and distributed across different
layers compared to those of verbal predicates.
We confirm our hunch by evaluating the ability
of a W-Avg probe trained on roleset identification
for verbal predicates only to also perform roleset
identification for nominal predicates in a zero-shot
fashion, and vice versa. Although, from a first
glance at the results reported in Table 3, our simple
model seems to be able to perform nominal role-
set identification after being trained only on verbal
XLM - R
m-B E R T
En gli sh v er b al p re d ic at e sCh ine se v er b al p re d ic at e s
2 4 6 8 10 12
0
5
10
15
20
25
30
35 Sense
Roleset
PLM layers
Relative importance (% )
2 4 6 8 10 12
0
5
10
15
20
S ense
Roleset
2 4 6 8 10 12
0
5
10
15
20
25
30
35 Sense
Roleset
PLM layers
Relative importance (% )
2 4 6 8 10 12
0
5
10
15
20
25
S ense
Roleset
PLM layers
Figure 2: Relative importance (%) of each hidden layer of multilingual BERT (left) and XLM-RoBERTa (right)
for sense probing and roleset probing. Results in English are in blue (top), whereas results in Chinese are in red
(bottom).
rolesets, the performance is actually worse than a
control probe, which is trained with a randomly
initialized model on nominal roleset identification.
In general, our analysis provides an empirical ex-
planation for why recent approaches for nominal
SRL
adapted from verbal
SRL
are still struggling
to learn general features across different predicate
types, despite initial promising results (Klein et al.,
2020;Zhao and Titov,2020).
3.4 Senses and Rolesets Across Languages
We conclude our analysis on predicate senses and
their rolesets with another important finding: mul-
tilingual
PLM
s encode both predicate sense and
roleset information at similar layers across two
very different languages, English and Chinese. In
order to support this statement, we train an W-Avg
probe on both sense disambiguation and roleset
identification, first on the English verbal predicates
from the training split of CoNLL-2009 and then
on the Chinese verbal predicates from the training
split of CoNLL-2009.
Figure 2shows the distributions of the learned
weights for each hidden layer of two language mod-
els, multilingual BERT (left) and XLM-RoBERTa
(right). In particular, we observe that the probe
learns to almost completely discard the first five
layers of multilingual BERT for roleset identifica-
tion in both English (top-left) and Chinese (bottom-
left), while assigning similar weights across En-
glish and Chinese to the other hidden layers, with
the 8th layer being relatively important in both lan-
guages. Overall, Figure 2supports the evidence
that both multilingual BERT and XLM-RoBERTa
encode the same type of “semantic knowledge” at
roughly the same hidden layers across languages,
supporting the findings by Conneau et al. (2020)
and indicating a possible direction for future work
in cross-lingual transfer learning for SRL.
4 Integrating Predicate-Argument
Structure Knowledge
Now that we have provided an in-depth look at
how sense and roleset information is encoded at
different inner layers of current
PLM
s (Section 3.2),
highlighted the differences in how
PLM
s encode
verbal and nominal predicates (Section 3.3), and
revealed that multilingual
PLM
s capture semantic
knowledge at similar layers across two diverse lan-
guages (Section 3.4), one may wonder how we can
take advantage in a practical setting of what we
have learned so far. In this Section, we study how
we can improve a modern system for end-to-end
SRL
by integrating sense and roleset knowledge
into its architecture.
4.1 Model Description
In what follows, we briefly describe the architec-
ture of our baseline model, which is based on that
proposed by Conia and Navigli (2020). Notice that,
even though we refer to this model as our baseline,
its end-to-end architecture rivals current state-of-
the-art approaches, such as Blloshmi et al. (2021),
Conia et al. (2021) and Paolini et al. (2021).
Given an input sentence
w
, the model computes
a contextual representation
xi
for each word
wi
in
w
by concatenating the representations obtained
from the four topmost layers of a pretrained lan-
guage model. These contextual word representa-
tions are then processed by a stack of “fully con-
nected” BiLSTM layers in which the input to the
i
-th BiLSTM layer is the concatenation of the in-
puts of all previous BiLSTM layers in the stack,
obtaining a sequence
h
of refined encodings. These
encodings
h
are made “predicate-aware” by con-
catenating each
hi
of
wi
to the representation
hp
of each predicate
p
in the sentence, and finally
processed by another stack of fully-connected BiL-
STMs, resulting in a sequence
a
of argument en-
codings. We refer to Conia and Navigli (2020) for
further details about the architecture of our baseline
model.
Enhancing the SRL model.
Based on our obser-
vations and analyses in the Sections above, we put
forward three simple enhancements to our strong
baseline model:
Representing words using a weighted average
of all the inner layers of the underlying lan-
guage model, since we now know that seman-
tic features important for the task are scattered
across all the layers of a PLM;
Using two different sets of weights to com-
pute different weighted averages for predicate
senses and predicate arguments, as semantic
features important for the two tasks are dis-
tributed differently across the inner layers of
the underlying PLM;
Adding a secondary task to predict rolesets
from a predicate representation
hp
in a multi-
task learning fashion.
P R F1
BERTbase – baseline 91.8 91.9 91.8
BERTbase – W-Avg 91.9 92.0 91.9
BERTbase – 2×W-Avg 92.1 92.1 92.1
BERTbase – 2×W-Avg + MT 92.2 92.2 92.2
BERTlarge – baseline 91.7 91.7 91.7
BERTlarge – W-Avg 91.9 92.0 92.0
BERTlarge – 2×W-Avg 92.5 92.5 92.5
BERTlarge – 2×W-Avg + MT 92.8 92.7 92.8
Table 4: Results in terms of micro-averaged precision,
recall and F1 score on SRL over the verbal predicate
instances in the standard gold benchmark of CoNLL-
2009 for dependency-based SRL.
Results on SRL.
Table 4compares the results
obtained on the verbal predicate instances in the
standard gold benchmark of CoNLL-2009 for
dependency-based
SRL
.
5
As we can see, each con-
tribution provides an improvement over the previ-
ous one, both when using BERT-base-cased and
BERT-large-cased (+0.4% and +1.1% in F1 score
6
over the baseline, respectively), the latter being
one of the most used pretrained language models
to achieve state-of-the-art results on the task. In
general, not only did our analysis shed light on
interesting properties of current
PLM
s through the
lens of predicate senses and their rolesets, but it
also provided practical hints on how to better ex-
ploit such properties in SRL.
Qualitative Analysis.
Finally, we provide a look
at what happens when our model is informed about
predicate senses and their rolesets at training time.
To inspect how the vector representations of pred-
icates change as we inject more inductive bias to-
wards predicate-argument information, in Figure 3
we use t-SNE to project and visualize on a bidimen-
sional plane the representations of the predicate
close when using: i) the baseline model, which is
unaware of predicate-argument information and,
therefore, does not show any significant cluster-
ing according to different rolesets; ii) the model
when it can use different weighted averages to com-
5
We trained our model for 30 epochs using Adam with
an initial learning rate of 1e-3, leaving all parameters of the
underlying language model frozen and using the parameter
values used in the original paper by Conia and Navigli (2020).
6
Scores were computed using the official CoNLL-2009
scorer provided during the shared task. This scoring script
produces a unified F1 measure that takes into account both
predicate senses and semantic roles.
AM-EX T AM-MNR/AM-TMP AM-E XT/AM-TMPAM-MNR AM-TMP
rolesets
AM-EX T/AM-MNR
Figure 3: t-SNE visualization of the representations for the predicate close. Different colors represent different
rolesets, even though some rolesets are partially overlapping (e.g. {AM-EXT, AM-MNR} and {AM-EXT, AM-
TMP}). From left to right: predicate representations from the baseline SRL model which is completely unaware of
rolesets (left); predicate representations from an SRL model that can use two different weighted averages to create
different representations for predicate senses and their arguments (center); predicate representations from an SRL
model that is tasked to explicitly identify rolesets through a secondary learning objective in a multi-task fashion
(right).
pute representations for predicate senses and their
arguments; and iii) the model when it is explic-
itly tasked with the secondary training objective of
learning to identify the roleset of each predicate.
As one can see, as we inject more linguistic infor-
mation into the model, the representations can be
clustered better according to their corresponding
predicate-argument structures.
5 Conclusion
In this paper, we probed PLMs for PASs: dif-
ferently from past work, we dissected SRL into
its core subtasks and analysed how PLMs encode
predicate-argument structure information such as
predicate senses and their rolesets. In our analysis,
we observed that, despite the intrinsic connection
between predicate senses and their rolesets that
exists in several popular SRL inventories, differ-
ent PLMs encode their features across significantly
different layers. What is more, we also discov-
ered that verbal and nominal predicates and their
PASs are represented differently, making verbal-
to-nominal SRL transfer far from trivial, and pro-
viding an empirical explanation for why previous
attempts in this direction have struggled to obtain
strong results. Furthermore, our analysis revealed
that current multilingual language models encode
PASs similarly across two very different languages,
namely, English and Chinese.
Finally, in contrast to previous work on probing,
we put together what we learned and demonstrated
a practical application of our findings by devising
simple yet effective techniques for the integration
of predicate-argument structure knowledge into a
state-of-the-art end-to-end architecture for SRL.
Acknowledgments
The authors gratefully acknowledge
the support of the ERC Consolida-
tor Grant MOUSSE No. 726487
and the European Language Grid
project No. 825627 (Universal Se-
mantic Annotator, USeA) under the
European Union’s Horizon 2020 re-
search and innovation programme.
This work was supported in part by the MIUR
under grant “Dipartimenti di Eccellenza 2018-
2022” of the Department of Computer Science of
Sapienza University of Rome.
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