Search Results for author: Simone Tedeschi

Found 11 papers, 8 papers with code

Named Entity Recognition for Entity Linking: What Works and What’s Next

1 code implementation Findings (EMNLP) 2021 Simone Tedeschi, Simone Conia, Francesco Cecconi, Roberto Navigli

Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models.

Entity Disambiguation Entity Linking +3

MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)

1 code implementation Findings (NAACL) 2022 Simone Tedeschi, Roberto Navigli

Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications.

Entity Linking named-entity-recognition +2

ID10M: Idiom Identification in 10 Languages

1 code implementation Findings (NAACL) 2022 Simone Tedeschi, Federico Martelli, Roberto Navigli

Idioms are phrases which present a figurative meaning that cannot be (completely) derived by looking at the meaning of their individual components. Identifying and understanding idioms in context is a crucial goal and a key challenge in a wide range of Natural Language Understanding tasks.

Natural Language Understanding

ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming

1 code implementation6 Apr 2024 Simone Tedeschi, Felix Friedrich, Patrick Schramowski, Kristian Kersting, Roberto Navigli, Huu Nguyen, Bo Li

When building Large Language Models (LLMs), it is paramount to bear safety in mind and protect them with guardrails.

RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset

1 code implementation16 Jun 2023 Pere-Lluís Huguet Cabot, Simone Tedeschi, Axel-Cyrille Ngonga Ngomo, Roberto Navigli

Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge.

Relation Relation Extraction

What's the Meaning of Superhuman Performance in Today's NLU?

no code implementations15 May 2023 Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajic, Daniel Hershcovich, Eduard H. Hovy, Alexander Koller, Simon Krek, Steven Schockaert, Rico Sennrich, Ekaterina Shutova, Roberto Navigli

In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension.

Position Reading Comprehension

Focusing on Context is NICE: Improving Overshadowed Entity Disambiguation

no code implementations12 Oct 2022 Vera Provatorova, Simone Tedeschi, Svitlana Vakulenko, Roberto Navigli, Evangelos Kanoulas

Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base.

Entity Disambiguation

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