I n V e R o: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles (2020)

Abstract: 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 pretrained 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 .

## introduction since its introduction @xcite , semantic role labeling (srl) has been recognized as a key task to enable natural language understanding in that it aims at explicitly answering the "who did what to whom, when and where?" question by identifying and labeling the predicateargument 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 question answering @xcite , information extraction @xcite , situation recognition @xcite , machine translation @xcite , and opinion role labeling @xcite . unfortunately, the integration of srl knowledge into downstream applications has often been hampered and slowed down by the intrinsic complexity of the task itself @xcite . indeed, srl is strongly intertwined with elaborate linguistic theories, as identifying and labeling predicateargument relations requires well-defined predicate sense and semantic role inventories such as the popular propbank @xcite , verbnet @xcite , or framenet @xcite . the linguistic intricacies of such resources may, however, dishearten and turn away new practitioners. 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 disambiguation, 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 @xcite @xcite . therefore, obtaining predicate sense and semantic role annotations necessitates the tedious orchestration of multiple automatic systems, which in its turn further complicates the use of srl in practice 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 semantic role labeling accessible to a broad audience. invero brings together resources and tools to perform human-readable srl, and it accomplishes this by using the intelligible verb senses and semantic roles of a recently proposed resource named verbatlas @xcite and exploiting them to annotate sentences with high performance. in more detail, the invero platform includes: notably, invero also takes advantage of propbank to get the best of both worlds, and provides annotations according to both resources, enabling comparability and fostering integration. ## the invero platform the invero platform aims at making srl more approachable to a wider audience, not only in order 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, indispensable for the definition of the task itself, and ii) the complexity of the recently proposed techniques. section 2.1 explains how invero takes advantage 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. ## the invero apis to foster the integration of semantics into a wider range of applications, the invero platform introduces a set of restful apis foot_4 that offer i) easy-touse abstractions to query resource-specific information in verbatlas (section 3.1), and ii) out-of-thebox predicate and semantic role annotations from a state-of-the-art pretrained model (section 3.2). ## the invero user interface like many other linguistic resources in srl, ver-batlas may be daunting for inexperienced practitioners who may still face difficulties in finding their way with the formalisms defined in a linguistic resource for srl. on top of the previously described apis (section 3) and in an effort to make verbatlas easier to interact with, the invero platform includes a public-facing web interface that provides a user-friendly environment to explore not only the functionalities offered by the resource, but also to understand visually how an srl system annotates 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 unified view, letting users perform resource-specific queries or annotate sentences wherever they are without writing a single line of code. resource interface. figure 1 shows the web interface when a user inserts the name of a verbatlas frame in the search bar. notice that, since the interface 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 predicate senses that share similar semantic behavior. each predicate sense is also conveniently linked to babelnet 4.0 @xcite , a multilingual knowledge graph where users can find more information such as hypernyms, hyponyms, and semantically 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 predicate/frame can bear in a sentence. ## 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 interested in exploring its integration into downstream applications. in this paper, we aim at ameliorating both of the issues by presenting the invero platform. invero features easy-to-use restful apis to effortlessy query verbatlas, a recently introduced linguistic resource for srl, and to transparently 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. notably, 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 argument classification -and it does not require external tools such as lemmatizers, part-of-speech taggers or syntactic tree parsers: users just have to provide a raw text sentence to obtain its corresponding predicate and argument labels. moreover, the invero platform includes an online web interface which repackages the apis in a user-friendly environment. thanks to this interface, users can easily 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, alongside the already available state-of-the-art spanbased model, the state-of-the-art dependency-based model of @xcite , 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 techniques for tasks where semantics is crucial @xcite @xcite @xcite , 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.