InVeRo-XL : M aking Cross-Lingual S emantic R ole L abeling Accessible with Intelligible Verbs and Roles (2021)

Abstract: Notwithstanding the growing interest in cross-lingual techniques for Natural Language Processing, there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling. In this paper, we fill this gap and present InVeRo-XL, an off-the-shelf state-of-the-art system capable of annotating text with predicate sense and semantic role labels from 7 predicate-argument structure inventories in more than 40 languages. We hope that our system – with its easy-to-use RESTful API and Web interface – will become a valuable tool for the research community, encouraging the integration of sentence-level semantics into cross-lingual downstream tasks. InVeRo-XL is available online at http://nlp.uniroma1.it/invero .

## introduction informally, semantic role labeling (srl) is often defined as the task of automatically answering the question "who did what, to whom, where, when, and how?" @xcite . more precisely, srl aims at recovering the predicateargument structures within a sentence, providing an explicit overlay that uncovers the underlying semantics of text. for this reason, srl is thought to be key in enabling natural language understanding @xcite . today srl is still an open problem, with several research papers being published each year at top-tier conferences, revealing novel insights and proposing better approaches. over the years, thanks to this active development, srl has been successfully exploited in a wide array of downstream tasks that span across different areas of artificial intelligence, from natural language processing (nlp) with information retrieval @xcite , question answering @xcite , machine translation @xcite and semantic parsing @xcite , to computer vision with visual semantic role labeling @xcite and situation recognition @xcite . recently, the growing interest in cross-lingual nlp, supported by the increasingly wide availability of pretrained multilingual language models such as bert @xcite and xlm-roberta @xcite , has sparked renewed interest in multilingual and cross-lingual srl. in just a few years, researchers have found ways to design fully-neural end-to-end systems for srl @xcite , to take advantage of contextual word representations @xcite , to achieve high performance on multiple languages @xcite , to generate sense and role labels with sequence-tosequence models @xcite and to perform srl jointly across heterogeneous inventories @xcite . since srl is a task that involves complex linguistic theories, inventories and techniques, there have been efforts to develop easy-to-use tools that offer automatic predicate sense and semantic role annotations to users interested in the integration of sentence-level semantics into downstream tasks. some notable examples include senna foot_0 @xcite , which uses an ensemble of feature-based classifiers @xcite , al-lennlp's srl demo foot_1 , which provides a reimplementation of a bert-based model @xcite , and invero @xcite , which offers annotations according to two different linguistic inventories, propbank @xcite and verbatlas @xcite . however, one important drawback of the above-mentioned tools is that they are able to perform srl only in english, which hinders the exploitation of their annotations in multilingual and cross-lingual nlp. in order to fill this gap, we build upon invero and propose its next major release, invero-xl, with the objective of making srl accessible in multiple languages. we rebuild invero-xl from the ground up to offer: we believe that invero-xl can provide a stepping stone for the integration of explicit sentence-level semantics into cross-lingual tasks, attracting new researchers to the field of srl and its applications. 3 ## system overview in this section, we provide an overview of the main components of invero-xl and how they interact, describing in detail the preprocessing module (section 2.1) and the srl model (section 2.2). ## the invero-xl api in order to facilitate the integration of predicateargument structure information into downstream tasks, invero-xl exposes its fully self-contained end-to-end multilingual srl pipeline through an easy-to-use restful api. in the following, we provide an overview of the main functionalities of the invero-xl api, from its resource api (section 3.1) to its model api (section 3.2) and how to host invero-xl locally on a user's own hardware (section 3.3). we refer users to the online documentation for the complete list of supported languages and inventories, together with other details. foot_4 ## web interface similarly to its predecessor, invero-xl includes a public-facing web interface (figures 2 and 3 ) that provides a visual environment for both the resource api and the model api, allowing users to explore the main functionalities while also providing an intuitive overview of how an srl system annotates a sentence or a short document. most importantly, the web interface of invero-xl has been updated to reflect the changes in the model api and the underlying srl model; now users can annotate text in 12 languages 11 and visualize predicate senses and semantic roles in 7 linguistic inventories on the fly, without having to write code. figure 3 shows an example sentence in italian with its corresponding predicate senses and seman-10 http://nlp.uniroma1.it/invero/api-documentation 11 we limit the number of languages available on the web interface due to hardware constraints as stanza and spacy use one preprocessing model for each language. figure 3 : the beginning of the divine comedy by dante alighieri in italian as tagged by invero-xl with three predicate-argument structure inventories -verbatlas, the chinese propbank and the spanish ancora. "nel mezzo del cammin di nostra vista mi ritrovai per una selva oscura, ché la diritta via era smarrita" translates into "midway upon the journey of our life i found myself within a forest dark, for the straightforward pathway had been lost". ## conclusion and future work over the years, the research community has greatly advanced the field of srl, proposing ever more complex approaches to tackle the task more effectively. however, despite the growing interest in cross-lingual nlp, there have been very few efforts to develop automatic tools to perform srl in multiple languages. our objective with invero-xl is to fill this gap and equip researchers with an easyto-use, high-performing system capable of providing predicate sense and semantic role annotations in over 40 languages with 7 linguistic inventories. users can take advantage of our state-of-the-art sys-tem for cross-lingual srl through a restful api that relieves them from the need to reimplement complex neural models and/or to build an efficient preprocessing/postprocessing pipeline. although invero-xl is a major step forward compared to its predecessor, we intend to further improve our system by adopting future and more advanced srl model architectures and by including new training datasets, such as united-srl @xcite . we strongly believe that invero-xl will facilitate the integration of srl into downstream cross-lingual tasks, hopefully aiding further advancements in cross-lingual natural language understanding.