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InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles

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Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts. To address this issue we present a new platform named Intelligible Verbs and Roles (InVeRo). This platform provides access to a new verb resource, VerbAtlas, and a state-of-the-art pre-trained implementation of a neural, span-based architecture for SRL. Both the resource and the system provide human-readable verb sense and semantic role information, with an easy to use Web interface and RESTful APIs available at http://nlp.uniroma1.it/invero.
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InVeRo: Making Semantic Role Labeling Accessible
with Intelligible Verbs and Roles
Simone Conia1,Fabrizio Brignone2,Davide Zanfardino2, and Roberto Navigli1
1Sapienza NLP Group, Department of Computer Science, Sapienza University of Rome
2Babelscape, Italy
lastname@di.uniroma1.it, lastname@babelscape.com
Abstract
Semantic Role Labeling (SRL) is deeply de-
pendent on complex linguistic resources and
sophisticated neural models, which makes the
task difficult to approach for non-experts. To
address this issue we present a new platform
named Intelligible Verbs and Roles (InVeRo).
This platform provides access to a new verb
resource, VerbAtlas, and a state-of-the-art pre-
trained implementation of a neural, span-based
architecture for SRL. Both the resource and
the system provide human-readable verb sense
and semantic role information, with an easy to
use Web interface and RESTful APIs available
at http://nlp.uniroma1.it/invero.
1 Introduction
Since its introduction (Gildea and Jurafsky,2002),
Semantic Role Labeling (SRL) has been recog-
nized as a key task to enable Natural Language Un-
derstanding in that it aims at explicitly answering
the "Who did What to Whom, When and Where?"
question by identifying and labeling the predicate-
argument structure of a sentence, namely, the actors
that take part in the scenario outlined by a predicate.
In fact, SRL has already proven to be useful in a
wide range of downstream tasks, including Ques-
tion Answering (Shen and Lapata,2007;He et al.,
2015), Information Extraction (Christensen et al.,
2011), Situation Recognition (Yatskar et al.,2016),
Machine Translation (Marcheggiani et al.,2018),
and Opinion Role Labeling (Zhang et al.,2019).
Unfortunately, the integration of SRL knowl-
edge into downstream applications has often been
hampered and slowed down by the intrinsic com-
plexity of the task itself (Navigli,2018). Indeed,
SRL is strongly intertwined with elaborate linguis-
tic theories, as identifying and labeling predicate-
argument relations requires well-defined predicate
sense and semantic role inventories such as the
popular PropBank (Palmer et al.,2005), VerbNet
(Kipper-Schuler,2005), or FrameNet (Baker et al.,
1998). The linguistic intricacies of such resources
may, however, dishearten and turn away new prac-
titioners. Regardless of which linguistic resource is
used in the task, to further complicate the situation
SRL has been usually divided into four subtasks
– predicate identification, predicate sense disam-
biguation, argument identification and argument
classification – but, to the best of our knowledge,
recent state-of-the-art systems do not address all
these four subtasks simultaneously without relying
on external systems (Swayamdipta et al.,2017;He
et al.,2018;Strubell et al.,2018;He et al.,2019).
Therefore, obtaining predicate sense and semantic
role annotations necessitates the tedious orchestra-
tion of multiple automatic systems, which in its
turn further complicates the use of SRL in prac-
tice and in semantics-first approaches to NLP more
generally.
In this paper, we present InVeRo (Intelligibile
Verbs and Roles), an online platform designed to
tackle the aforementioned issues and make Seman-
tic Role Labeling accessible to a broad audience.
InVeRo brings together resources and tools to per-
form human-readable SRL, and it accomplishes
this by using the intelligible verb senses and se-
mantic roles of a recently proposed resource named
VerbAtlas (Di Fabio et al.,2019) and exploiting
them to annotate sentences with high performance.
In more detail, the InVeRo platform includes:
a Resource API to obtain linguistic informa-
tion about the verb senses and semantic roles
in VerbAtlas.
a Model API to effortlessly annotate sentences
using a state-of-the-art end-to-end pretrained
model for span-based SRL.
a Web interface where users can easily query
linguistic information and automatically an-
notate sentences on-the-go without having to
write a single line of code.
Notably, InVeRo also takes advantage of PropBank
to get the best of both worlds, and provides an-
notations according to both resources, enabling
comparability and fostering integration.
2 The InVeRo Platform
The InVeRo platform aims at making SRL more
approachable to a wider audience, not only in or-
der to promote advances in the area of SRL itself,
but also to encourage the integration of semantics
into other fields of NLP. The two main barriers to
this objective are the complexity of i) the linguistic
resources used in SRL which are, however, indis-
pensable for the definition of the task itself, and ii)
the complexity of the recently proposed techniques.
Section 2.1 explains how InVeRo takes advan-
tage of the intelligible verb senses and semantic
roles of VerbAtlas to gently introduce non-expert
users to SRL, while Section 2.2 details how the
InVeRo model for SRL can make semantic role
annotations accessible to everyone.
2.1 Intelligible Verb Senses and Roles
One of the most contentious points of discussion in
SRL is how to formalize predicate-argument struc-
tures, that is, the semantic roles that actors can
play in a scenario defined by a predicate. Prop-
Bank (Palmer et al.,2005), one of the most popular
predicate-argument structure inventories, uses an
enumerative approach where each predicate sense
has a possibly different roleset, e.g., for the predi-
cate make, the sense make.01 (as in “making a prod-
uct”) bears the semantic roles ARG 0(creator), ARG 1
(creation), ARG 2(created from) and ARG 3(benefi-
ciary), whereas make.02 (as in “cause to be”) bears
only ARG 0(impeller) and ARG 1(impelled). This
exhaustive approach, however, requires an expert
linguist to tell which roles share similar seman-
tics across senses (e.g., AR G0is an agent in both
make.01 and make.02) and which do not (e.g., ARG1
is a product in make.01 but a result in make.02).
On the other hand, VerbAtlas (Di Fabio et al.,
2019), a recently proposed predicate-argument
structure inventory, in contrast to the enumerative
approach of PropBank and the thousands of frame-
specific roles of FrameNet, adopts a small set of ex-
plicit and intelligible semantic roles (AGE NT,PROD-
UCT,RESU LT,DES TIN ATION ,
. . .
,THE ME) inspired by
VerbNet (Kipper-Schuler,2005). As a result, in
VerbAtlas, whenever two predicate senses can bear
the same semantic role, the semantics of this role
is coherent across the two predicate senses by defi-
nition, resulting in readable labels for non-expert
users. VerbAtlas also clusters predicate senses into
so-called frames (COO K,DRINK,HIT, etc.) inspired
by FrameNet (Baker et al.,1998), with the idea
that senses sharing similar semantic behavior lie in
the same frame. For non-expert users, this organi-
zation has the added advantage of explicitly link-
ing predicate senses that are otherwise unrelated,
like make.01 and create.01 in PropBank which, in-
stead, are part of the same frame MOU NT-A SSEMB LE-
PROD UCE in VerbAtlas and, therefore, also share the
same semantic roles. In a bid to make SRL more
accessible, the InVeRo platform adopts the intelli-
gible verb senses and semantic roles of VerbAtlas.
2.2 An All-in-One Solution for SRL
As already mentioned in Section 1, the traditional
SRL pipeline consists of four main steps: predicate
identification, predicate sense disambiguation, ar-
gument identification and argument classification.
While some of the above steps are considered easier
than others, each of them features distinct peculiar-
ities, which has driven recent works to focus on
improving only specific aspects of the entire SRL
pipeline. Instead, little attention has been paid to
systems that can tackle all the above-mentioned
steps at the same time. As a result, anyone wishing
to take advantage of SRL annotations in another
NLP task has to choose, mix and match multiple
automatic systems in order to obtain sentences fully
annotated with predicate sense and semantic role
labels. Understandably, this has been a major de-
terrent for the integration of semantics into down-
stream applications.
As part of the InVeRo platform, not only do
we introduce an all-in-one model that addresses
the complete SRL pipeline with a single forward
pass, but we also make this model available through
a Web interface to let everyone label sentences
with SRL annotations without the need to install
any software. In other words, a user only has to
provide a raw text sentence; the InVeRo all-in-one
model for SRL takes care of the rest, making the
predicate sense and role labeling process accessible
and effortless.
Model Design.
The InVeRo all-in-one system for
SRL is based on the ideas put forward by He et al.
(2018) in that, unlike other works that used word-
level BIO tagging schemes to label arguments (He
et al.,2017;Strubell et al.,2018;Tan et al.,2018),
it directly models span-level features. In particular,
we follow He et al. (2018) by letting the neural
model learn span-level representations from the
word-level representations of the span start and
span end words, while also adding a span-length
specific trainable embedding. More formally, the
span representation
sij
from word
i
to word
j
is
obtained as follows:
sij =Ws(ew
iew
jel
ji) + bs
where
ew
i
and
ew
j
are the word representations of
start and end of the span,
el
ji
is the span length
embedding, and is the concatenation operation.
However, our approach features a few key dif-
ferences that set the InVeRo model apart from the
aforementioned works. First, it creates contextu-
alized word representations from the inner states
of BERT (
bert-base-cased
), a recent language
model trained on massive amounts of textual data
(Devlin et al.,2018). Differently from the recent
work of Shi and Lin (2019), our model takes ad-
vantage of the topmost four layers of BERT and
directly builds a word representation from its sub-
word representations, similarly to Bevilacqua and
Navigli (2020). More formally, given the BERT
representations
hk
ij
at layer
k
of the
mi
subwords
wij in word wi, with 1jmi:
cij =h1
ij h2
ij h3
ij h4
ij
c0
ij =ReLU(Wccij +bc)
ew
i=1
miX
j
c0
ij
Second, in contrast to other span-based SRL sys-
tems, our model integrates predicate disambigua-
tion as an additional objective in a multitask fash-
ion (Caruana,1997). Third, our model is trained to
jointly learn to label sentences with both VerbAtlas
and PropBank so as to exploit the complementary
knowledge of the two resources, and, at the same
time, provide a means to directly compare the pred-
icate sense and semantic role labels of two different
inventories for the same input sentences.1
Comparison with previous systems.
Over the
years, several SRL systems have been developed
1
We used the PropBank-to-VerbAtlas mappings avail-
able at
http://verbatlas.org/download
to remap
CoNLL-2012.
and made available as prepackaged downloads, e.g.
SENNA
2
, or as online demos, e.g., AllenNLP’s
SRL demo
3
. However, recent BERT-based online
systems, such as AllenNLP’s SRL demo, do not
perform predicate sense disambiguation (in addi-
tion to predicate identification, argument identi-
fication and argument classification), which is a
crucial step in SRL, especially when considering
that the PropBank roles ARG 0,ARG 1, through ARG 5
become meaningful only if they are associated with
a PropBank predicate sense (see Section 2.1).
Results.
Thanks to the use of contextualized
word representations from BERT, the joint exploita-
tion of two complementary linguistic resources for
SRL, and the introduction of a predicate sense dis-
ambiguation layer, our model achieves 84.0% in
F
1
score in the standard argument identification
and classification test split of the CoNLL-2012
dataset (Pradhan et al.,2012), significantly outper-
forming the previous state of the art among end-
to-end models, currently represented by Strubell
et al. (2018) with a 0.6% absolute improvement
in F
1
score
4
(84.0% against 83.4%). We note that
this measure does not take into account the perfor-
mance on predicate sense disambiguation, where
our system achieves 86.1% in F
1
score, which is
a significant absolute improvement (+5.7%) over
the most-frequent-sense strategy (86.1% against
80.4%).
3 The InVeRo APIs
To foster the integration of semantics into a wider
range of applications, the InVeRo platform intro-
duces a set of RESTful APIs
5
that offer i) easy-to-
use abstractions to query resource-specific informa-
tion in VerbAtlas (Section 3.1), and ii) out-of-the-
box predicate and semantic role annotations from
a state-of-the-art pretrained model (Section 3.2).
3.1 Resource API
The Resource API provides a RESTful interface to
easily link predicate-level information, e.g., predi-
cate lemmas and/or predicate senses, to VerbAtlas-
specific features, e.g., frames and semantic roles.
In particular:
2https://ronan.collobert.com/senna
3https://demo.allennlp.org/
semantic-role- labeling
4Score computed with the official CoNLL-2005 script.
5http://nlp.uniroma1.it/invero/
api-documentation
the
/predicate
endpoint exposes functionali-
ties to obtain frame-level information start-
ing from a predicate lemma or a synset
from WordNet 3.0 (Fellbaum et al.,1998) or
BabelNet 4.0 (Navigli and Ponzetto,2012);
the
/frame
endpoint exposes functionalities
to retrieve, for a given frame, its Predicate Ar-
gument Structure, and the WordNet/BabelNet
synsets belonging to this frame.
Also included is a manually-curated PropBank-to-
VerbAtlas alignment to remap existing corpora like
the CoNLL-2009 and CoNLL-2012 datasets. In
particular:
the
/align/sense
endpoint returns, for a
given PropBank predicate sense, its corre-
sponding VerbAtlas frame, i.e., the VerbAt-
las frame that generalizes the given PropBank
predicate sense;
the
/align/roles
endpoint returns, for a
given PropBank predicate sense, e.g., aim.01,
the alignment of each role in the PropBank ar-
gument structure of the given predicate sense
to a VerbAtlas role, e.g., AR G0
AGE NT,AR G1
THE ME, and so on.
The online documentation provides an overview of
the accepted parameters at the endpoints available
in the Resource API.
3.2 Model API
To encourage the integration of SRL into down-
stream applications, the Model API offers a simple
solution for out-of-the-box role labeling by provid-
ing an interface to a full end-to-end state-of-the-art
pretrained model. Unlike most currently available
models which focus on specific aspects of the entire
SRL task, our solution jointly addresses in a single
forward pass the whole traditional SRL pipeline,
namely, i) predicate identification, ii) predicate
sense disambiguation, iii) argument identification,
and iv) argument classification. Furthermore, our
model is fully self-contained as it does not require
any of the additional linguistic information, from
lemmatization to part-of-speech tags and syntac-
tic parse trees, that are usually exploited by many
systems. Our Model API is:
Easy to use:
an end user avoids the struggle
of mixing and matching a set of automatic
systems where each system independently ad-
dresses a different part of the SRL pipeline;
Fully self-contained:
the only input to the
underlying model is a raw text sentence, drop-
ping any dependency on external preprocess-
ing tools;
State-of-the-Art:
the underlying model car-
ries out SRL with high performances on the
standard CoNLL-2012 benchmark dataset.
Usage.
The Model API exposes a single endpoint
named
/model/
which accepts
GET
requests with a
single parameter named
sentence
containing the
raw text sentence to label with semantic role annota-
tions. The Model API returns a
JSON
response that
contains, for each predicate it identifies in the sen-
tence, the semantic role that each argument plays
with respect to the identified predicate. For exam-
ple, the response for the sentence “Eliminating the
income tax will benefit peasants" contains:
[{
"tokenIndex":0,
...
},{
"tokenIndex":5,
"verbatlas":{
"frameName":"HELP_HEAL_CARE_CURE",
"roles":[
{"role":"agent","score":0.89,"span":[0,4]},
{"role":"beneficiary","score":1.,"span":[6,7]}
]
},
"propbank":{
"frameName":"benefit.01",
"roles":[
{"role":"ARG0","score":0.97,"span":[0,4]},
{"role":"ARG1","score":1.00,"span":[6,7]}
]
},
}]
Our Model API also supports the more popular
PropBank predicate sense and semantic role labels
so as to provide a direct comparison with VerbAtlas
and promote synergistic approaches that exploit
both inventories to advance SRL.
4 The InVeRo User Interface
Like many other linguistic resources in SRL, Ver-
bAtlas may be daunting for inexperienced practi-
tioners who may still face difficulties in finding
their way with the formalisms defined in a linguis-
tic resource for SRL. On top of the previously de-
scribed APIs (Section 3) and in an effort to make
VerbAtlas easier to interact with, the InVeRo plat-
form includes a public-facing Web interface that
provides a user-friendly environment to explore not
only the functionalities offered by the resource, but
Figure 1: A look at the online interface when a user searches for resource-specific information about VerbAtlas.
The user can a) search for a frame name, as in the Figure, or an individual predicate. The interface displays
b) all the predicates belonging to the same frame, with each predicate c) directly linked to BabelNet. The right
side displays the d) selected predicate with e) its WordNet gloss, f) the semantic roles of its predicate-argument
structure, and g) the selectional preferences of each role.
Figure 2: A look at the online interface when a user inserts a sentence in the search bar. The system uses a
pretrained model to display all the information of all the steps of a traditional SRL pipeline: predicate identification,
predicate sense disambiguation, argument identification and argument classification.
Figure 3: The interface can seamlessly switch between VerbAtlas and PropBank labels with a single click (the
switch button at the top-right). Here we show the same sentence as in Figure 2but labeled with PropBank predicates
and roles, which enables comparison across the two annotation styles.
also to understand visually how an SRL system an-
notates a sentence in a live interactive demo. The
Web interface mirrors the functionalities of both the
Resource API and the Model API in a minimal uni-
fied view, letting users perform resource-specific
queries or annotate sentences wherever they are
without writing a single line of code.
Resource interface. Figure 1shows the Web in-
terface when a user inserts the name of a VerbAtlas
frame in the search bar. Notice that, since the inter-
face makes use of the Resource API, a user can also
search for other resource-specific information such
as individual predicates. Particular attention has
been given to the visualization of a VerbAtlas frame
(Figure 1, left side) which displays all the predi-
cate senses that share similar semantic behavior.
Each predicate sense is also conveniently linked to
BabelNet 4.0 (Navigli and Ponzetto,2012), a multi-
lingual knowledge graph where users can find more
information such as hypernyms, hyponyms, and se-
mantically related concepts. Equally important is
the visualization of a VerbAtlas predicate-argument
structure (Figure 1, right side) which displays all
the semantic roles that the currently selected predi-
cate/frame can bear in a sentence.
Model interface.
Figure 2shows, instead, the
online model interface when a user inserts a sen-
tence with its corresponding predicate sense and
semantic role labels from VerbAtlas. Notice how
the user can quickly switch between the VerbAtlas
and the PropBank predicate sense and semantic
role annotations with just a single click, so that the
two annotation styles can easily be compared one
with the other (Figures 2and 3). To the best of
our knowledge, this is the first online demo where
a neural model helps users visualize all the four
steps of the traditional SRL pipeline for two dif-
ferent linguistic resources for SRL, VerbAtlas and
PropBank, at the same time.
5 Conclusion and Future Work
Semantic Role Labeling is deeply dependent on
complex linguistic resources and elaborate neural
models: the combination of these two factors has
made Semantic Role Labeling (SRL) difficult to
approach for experts from other fields who are inter-
ested in exploring its integration into downstream
applications. In this paper, we aim at ameliorat-
ing both of the issues by presenting the InVeRo
platform. InVeRo features easy-to-use RESTful
APIs to effortlessy query VerbAtlas, a recently in-
troduced linguistic resource for SRL, and to trans-
parently use a pretrained state-of-the-art end-to-end
system for the recent VerbAtlas-style and the more
traditional PropBank-style approaches to SRL. No-
tably, the InVeRo system is fully self-contained
as it tackles all the steps of the traditional SRL
pipeline – predicate identification, predicate sense
disambiguation, argument identification, and argu-
ment classification – and it does not require ex-
ternal tools such as lemmatizers, part-of-speech
taggers or syntactic tree parsers: users just have to
provide a raw text sentence to obtain its correspond-
ing predicate and argument labels. Moreover, the
InVeRo platform includes an online Web interface
which repackages the APIs in a user-friendly envi-
ronment. Thanks to this interface, users can eas-
ily obtain human-readable linguistic information
about VerbAtlas, but also annotate entire sentences
on-the-go without the need to install any software.
InVeRo is a growing platform: in the future, we
plan to enhance our Model API by adding, along-
side the already available state-of-the-art span-
based model, the state-of-the-art dependency-based
model of Conia and Navigli (2020a), so that users
can easily switch between the two approaches and
choose the one that best suits their needs. Thanks to
BabelNet and recent advances in cross-lingual tech-
niques for tasks where semantics is crucial (Barba
et al.,2020;Blloshmi et al.,2020;Conia and Nav-
igli,2020b;Pasini,2020;Scarlini et al.,2020), we
also plan to provide support for multiple languages
to enable SRL integration into multilingual and
cross-lingual settings. We believe that the InVeRo
platform can make SRL more accessible to the
research community, and we look forward to the
development of semantics-first approaches in an
ever wider range of NLP applications.
Acknowledgments
The authors gratefully acknowledge
the support of the ERC Consolidator
Grant MOUSSE No. 726487 under
the European Union’s Horizon 2020
research 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.
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... Furthermore, we annotated part of the British English corpus of CHILDES with semantic roles for allowing its usage as a training dataset for fine-tuning PictoBERT to perform pictogram prediction based on a semantic structure, in an approach similar to Pereira et al. (2020). For doing so, we used InVeRo (Conia et al., 2020) and SLING (Ringgaard et al., 2017) as semantic parsers. We used InVeRo for predicate disambiguation to identify the sentence's predicates and its relative semantic frames. ...
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