Papers by Riccardo Orlando
RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation (2025.emnlp-main)
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| Challenge: | Entity Linking and Entity Disambiguation systems assume static knowledge bases are incomplete and up-to-date, rendering them incapable of handling entities not yet included in the knowledge base. |
| Approach: | They propose a model that retrieves external knowledge to improve factual grounding in entity descriptions. |
| Outcome: | The proposed model outperforms systems that require fixed knowledge sets on Entity Disambiguation and Wikipedia to improve factual grounding in entity descriptions. |
MOSAICo: a Multilingual Open-text Semantically Annotated Interlinked Corpus (2024.naacl-long)
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Simone Conia, Edoardo Barba, Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Riccardo Orlando, Luigi Procopio, Roberto Navigli
| Challenge: | Existing approaches to integrate semantics into Natural Language Understanding (NLP) systems are cost-effective and environmental impact-related. |
| Approach: | They propose to provide semantically-annotated corpora for four NLU tasks across five languages and to drop the requirement of closed datasets. |
| Outcome: | The proposed model provides hundreds of millions of silver yet high-quality annotations for four NLU tasks across five languages. |
ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering (2024.emnlp-main)
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| Challenge: | Current Large Language Models (LLMs) have shown strong reasoning capabilities in commonsense question answering benchmarks, but the process underlying their success remains largely opaque. |
| Approach: | They propose a zero-shot question answering framework that combines retrieval, case-based reasoning and introspection to improve the model's performance and interpretability. |
| Outcome: | The proposed framework outperforms existing LLMs and previous knowledge integration approaches in commonsense reasoning benchmarks and achieves an average accuracy improvement of 4.5 points. |
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities (2023.findings-acl)
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| Challenge: | Existing systems for SRL are incapable of transferring knowledge across different predicate types. |
| Approach: | They propose a new PropBank dataset which boasts wide coverage of multiple predicate types and a manually-annotated challenge set which gives equal importance to verbal, nominal, and adjectival predicates. |
| Outcome: | The proposed dataset shows that standard benchmarks do not provide an accurate picture of the current situation in SRL and that state-of-the-art systems are still incapable of transferring knowledge across different predicate types. |
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget (2024.findings-acl)
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| Challenge: | Entity Linking and Relation Extraction (EL) are fundamental tasks in Natural Language Processing. |
| Approach: | They propose a Retriever-Reader architecture for Entity Linking and Relation Extraction . they propose an input representation that incorporates the candidate entities alongside the text . |
| Outcome: | The proposed architecture achieves state-of-the-art in in- and out-of domain benchmarks while using academic budget training and with 40x inference speed compared to competitors. |
Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing (2022.lrec-1)
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| Challenge: | Existing approaches to understanding textual information are still far from achieving true natural language understanding (NLU). |
| Approach: | They propose a unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantics Parsing. |
| Outcome: | The proposed system can provide users with rich and diverse semantic information, help second-language learners, and integrate explicit semantic knowledge into downstream tasks and real-world applications. |
InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles (2021.emnlp-demo)
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| Challenge: | InVeRo-XL is an off-the-shelf system capable of annotating text with predicate sense and semantic role labels from 7 predicated-argument structure inventories in more than 40 languages. |
| Approach: | They propose to use RESTful API and Web interface to integrate sentence-level semantics into cross-lingual downstream tasks. |
| Outcome: | The proposed system can annotate text with predicate sense and semantic role labels from 7 predicated-argument structure inventories in more than 40 languages. |
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation (2021.emnlp-demo)
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| Challenge: | Word Sense Disambiguation (WSD) is a task of associating a word in context with its most appropriate sense from a predefined sense inventory. |
| Approach: | They propose to use a state-of-the-art neural model to integrate WSD into real-world applications. |
| Outcome: | The proposed system offers high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. |