- EMNLPInVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and RolesConia Simone, Orlando Riccardo, Brignone Fabrizio, Cecconi Francesco, and Navigli RobertoIn Proceedings of EMNLP: System Demonstrations, 2021.
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.
- EMNLPAMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense DisambiguationOrlando Riccardo, Conia Simone, Brignone Fabrizio, Cecconi Francesco, and Navigli RobertoIn Proceedings of EMNLP: System Demonstrations, 2021.
Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models. Unfortunately, such systems are still not available as ready-to-use end-to-end packages, making it difficult for researchers to take advantage of their performance. The only alternative for a user interested in applying WSD to downstream tasks is to use currently available end-to-end WSD systems, which, however, still rely on graph-based heuristics or non-neural machine learning algorithms. In this paper, we fill this gap and propose AMuSE-WSD, the first end-to-end system to offer high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. We hope that AMuSE-WSD will provide a stepping stone for the integration of meaning into real-world applications and encourage further studies in lexical semantics. AMuSE-WSD is available online at http://nlp.uniroma1.it/amuse-wsd.
- EMNLPNamed Entity Recognition for Entity Linking: What Works and What’s NextTedeschi Simone, Conia Simone, Cecconi Francesco, and Navigli RobertoIn Findings of the Association for Computational Linguistics: EMNLP 2021, 2021.
Entity Linking (EL) systems have achieved impressive results on standard benchmarks, mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data — millions of labeled examples — to perform at their best, with training times that often exceed several days, especially when limited computational resources are available. In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. More specifically, we show how and to what extent an EL system can benefit from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities. We release our software — code and model checkpoints — at https://github. com/Babelscape/ner4el.
- EMNLPUniteD-SRL: A Unified Dataset for Span-and Dependency-Based Multilingual and Cross-Lingual Semantic Role LabelingTripodi Rocco, Conia Simone, and Navigli RobertoIn Findings of the Association for Computational Linguistics: EMNLP 2021, 2021.
Multilingual and cross-lingual Semantic Role Labeling (SRL) have recently garnered increasing attention as multilingual text representation techniques have become more effective and widely available. While recent work has attained growing success, results on gold multilingual benchmarks are still not easily comparable across languages, making it difficult to grasp where we stand. For example, in CoNLL-2009, the standard benchmark for multilingual SRL, language-to-language comparisons are affected by the fact that each language has its own dataset which differs from the others in size, domains, sets of labels and annotation guidelines. In this paper, we address this issue and propose UNITED-SRL, a new benchmark for multilingual and cross-lingual, span-and dependency-based SRL. UNITED-SRL provides expert-curated parallel annotations using a common predicate-argument structure inventory, allowing direct comparisons across languages and encouraging studies on cross-lingual transfer in SRL. We release UNITED-SRL v1.0 at https://github.com/SapienzaNLP/united-srl.
- IJCAITen Years of BabelNet: A SurveyIn Proceedings of IJCAI, 2021.
The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI. However, when it comes to Natural Language Processing (NLP), symbols have to be mapped to words and phrases, which are not only ambiguous but also language-specific: multilinguality is indeed a desirable property for NLP systems, and one which enables the generalization of tasks where multiple languages need to be dealt with, without translating text. In this paper we survey BabelNet, a popular wide-coverage lexical-semantic knowledge resource obtained by merging heterogeneous sources into a unified semantic network that helps to scale tasks and applications to hundreds of languages. Over its ten years of existence, thanks to its promise to interconnect languages and resources in structured form, BabelNet has been employed in countless ways and directions. We first introduce the BabelNet model, its components and statistics, and then overview its successful use in a wide range of tasks in NLP as well as in other fields of AI.
- IJCAIGenerating Senses and RoLes: An End-to-End Model for Dependency-and Span-based Semantic Role LabelingIn Proceedings of IJCAI, 2021.
Despite the recent great success of the sequence-to-sequence paradigm in Natural Language Processing, the majority of current studies in Semantic Role Labeling (SRL) still frame the problem as a sequence labeling task. In this paper we go against the flow and propose GSRL (Generating Senses and RoLes), the first sequence-to-sequence model for end-to-end SRL. Our approach benefits from recently-proposed decoder-side pretraining techniques to generate both sense and role labels for all the predicates in an input sentence at once, in an end-to-end fashion. Evaluated on standard gold benchmarks, GSRL achieves state-of-the-art results in both dependency- and span-based English SRL, proving empirically that our simple generation-based model can learn to produce complex predicate-argument structures. Finally, we propose a framework for evaluating the robustness of an SRL model in a variety of synthetic low-resource scenarios which can aid human annotators in the creation of better, more diverse, and more challenging gold datasets. We release GSRL at github.com/SapienzaNLP/gsrl.
- NAACLUnifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic ResourcesConia Simone, Bacciu Andrea, and Navigli RobertoIn Proceedings of NAACL, 2021.
While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques. We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios. Most interestingly, our unified model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages.
- EACLFraming Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge IntegrationIn Proceedings of EACL, 2021.
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, especially if the sense inventory of choice is fine-grained. In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word. Not only does our simple method bear a closer resemblance to how human annotators disambiguate text, but it can also be extended seamlessly to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD.
- COLINGBridging the Gap in Multilingual Semantic Role Labeling: A Language-Agnostic ApproachIn Proceedings of COLING, 2020.
Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between high-resource (e.g., English) and low-resource (e.g., German) settings. To overcome this issue, we propose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. Our approach outperforms the state of the art in all the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of training data is available. Our objective is not to reject approaches that rely on syntax, rather to set a strong and consistent language-independent baseline for future innovations in Semantic Role Labeling. We release our model code and checkpoints at https://github.com/SapienzaNLP/multi-srl.
- COLINGConception: Multilingually-Enhanced, Human-Readable Concept Vector RepresentationsIn Proceedings of COLING, 2020.
To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amount of data available across languages. In this paper we address this issue and propose Conception, a novel technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. Our approach results in high-coverage representations that outperform the state of the art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation, proving particularly robust on low-resource languages. Conception - its software and the complete set of representations - is available at https://github.com/SapienzaNLP/conception.
- EMNLPInVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and RolesConia Simone, Brignone Fabrizio, Zanfardino Davide, and Navigli RobertoIn Proceedings of EMNLP, 2020.
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.
- EMNLPVerbAtlas: A Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role LabelingIn Proceedings of EMNLP, 2019.
We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments. We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to improvements on both the in-domain and out-of-domain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org.