--- title: "Roberto Navigli — papers and cards" description: "Roberto Navigli: 5 paper cards in the Research library (2017–2024), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/roberto-navigli/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/roberto-navigli/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Roberto Navigli # Roberto Navigli 5 paper cards in the Research library · 2017–2024 Roberto Navigli wrote 5 of the papers Mova reads, in 2017–2024, most of them on [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/), [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/), [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/); often with [Simone Conia](https://md.research.elamur.ai/free/en/authors/simone-conia/), Alessandro Raganato, Andrea Di Fabio. [OpenAlex](https://openalex.org/A5026648049) · [ORCID](https://orcid.org/0000-0003-3831-9706) · [**h-index 61**](https://openalex.org/authors/A5026648049) · [18860 citations](https://openalex.org/authors/A5026648049) · [555 works](https://openalex.org/works?filter=author.id:A5026648049) — openalex-snapshot, 2026-10-14 The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Roberto+Navigli) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Roberto+Navigli) · [dblp](https://dblp.org/search?q=Roberto+Navigli) · [ACL Anthology](https://aclanthology.org/search/?q=Roberto+Navigli) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Roberto+Navigli) Topics: [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/) 2 · [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/) 2 · [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) 1 ## Papers 1. **[Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics](https://md.research.elamur.ai/free/en/papers/perrella-2024-beyond-correlation-interpretable-evaluation-machine/)** *(2024)* Correlation with human judgement says little about what a metric score means. The paper evaluates metrics in two proxy scenarios: as binary classifiers for data filtering (good against bad, and perfect against other, labelled from WMT23 MQM with cut-offs set by MQM error weights), and as re-rankers, where the… 2. **[Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities](https://md.research.elamur.ai/free/en/papers/orlando-2023-exploring-non-verbal-predicates-semantic/)** *(2023)* Most SRL research and benchmarks assume that predicates are verbs. The authors test a state-of-the-art PropBank system (CN-22) trained on OntoNotes. Its argument F1 falls from 84.7 on verbs to 16.4 on nouns, and a noun-trained model falls from 72.8 to 11.2 on verbs, so it does not transfer knowledge across predicate… 3. **[Generationary or «How We Went beyond Word Sense Inventories and Learned to Gloss»](https://md.research.elamur.ai/free/en/papers/lex-2020-bevilacqua-generationary/)** *(2020)* The paper replaces the choice from a sense inventory with generation of a definition for a word in its context. 4. **[VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/fabio-2019-verbatlas-novel-large-scale-verbal/)** *(2019)* VerbAtlas clusters all 13,767 WordNet verb synsets by hand into 466 frames (EAT, KILL, GIVE, …; about 30 synsets each), each with one prototypical argument structure over 25 cross-frame roles taken from VerbNet (14 rare VerbNet roles merged into coarser ones, e.g. 5. **[Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison](https://md.research.elamur.ai/free/en/papers/onto-2017-raganato-word-sense-disambiguation-unified-evaluation/)** *(2017)* Results for all-words word sense disambiguation (WSD) are hard to compare, because the datasets, sense-inventory versions and preprocessing differ. ## Co-authors (11) - [Simone Conia](https://md.research.elamur.ai/free/en/authors/simone-conia/) ×2 - Alessandro Raganato - Andrea Di Fabio - Edoardo Barba - Jose Camacho-Collados - Lorenzo Proietti - Marco Maru - Michele Bevilacqua - Pere-Lluís Huguet Cabot - Riccardo Orlando - Stefano Perrella ## More papers in the library (11) Pages of the Research library: our summary, the ideas, references and who cites each. - [Cultural and Knowledge Biases in LLMs through the Lens of Entity-Aware Machine Translation](https://md.research.elamur.ai/free/en/papers/xu-2026-cultural-knowledge-biases-llms-through/) — Lu Xu, Luca Moroni, Roberto Navigli · 2026 - [Mind the Language Gap: Assessing LLM Safety in Italian](https://md.research.elamur.ai/free/en/papers/marafatto-2026-mind-language-gap-assessing-llm/) — Elena Marafatto, Roberto Navigli · 2026 - [DiBiMT: A Gold Evaluation Benchmark for Studying Lexical Ambiguity in Machine Translation](https://md.research.elamur.ai/free/en/papers/martelli-2025-dibimt-gold-evaluation-benchmark-studying/) — Federico Martelli, Stefano Perrella, Niccolò Campolungo, et al. · 2025 - [Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!](https://md.research.elamur.ai/free/en/papers/perrella-2024-guardians-machine-translation-meta-evaluation/) — Stefano Perrella, Lorenzo Proietti, Alessandro Scirè, et al. · 2024 - [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://md.research.elamur.ai/free/en/papers/martinelli-2024-maverick-efficient-accurate-coreference-resolution/) — Giuliano Martinelli, Edoardo Barba, Roberto Navigli · 2024 - [ID10M: Idiom Identification in 10 Languages](https://md.research.elamur.ai/free/en/papers/tedeschi-2022-id10m-idiom-identification-10-languages/) — Simone Tedeschi, Federico Martelli, Roberto Navigli · 2022 - [MaTESe: Machine Translation Evaluation as a Sequence Tagging Problem](https://md.research.elamur.ai/free/en/papers/perrella-2022-matese-machine-translation-evaluation-as/) — Stefano Perrella, Lorenzo Proietti, Alessandro Scirè, et al. · 2022 - [Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/) — Aidan Hogan, Eva Blomqvist, Michael Cochez, et al. · 2020 - [SensEmbed: Learning Sense Embeddings for Word and Relational Similarity](https://md.research.elamur.ai/free/en/papers/iacobacci-2015-sensembed-learning-sense-embeddings-word/) — Ignacio Iacobacci, Mohammad Taher Pilehvar, Roberto Navigli · 2015 - [SemEval-2007 Task 07: Coarse-Grained English All-Words Task](https://md.research.elamur.ai/free/en/papers/navigli-2007-semeval-2007-task-07-coarse/) — Roberto Navigli, Kenneth C. Litkowski, Orin Hargraves · 2007 - [SemEval-2007 Task 10: English Lexical Substitution Task](https://md.research.elamur.ai/free/en/papers/mccarthy-2007-semeval-2007-task-10-english/) — Diana McCarthy, Roberto Navigli · 2007