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Simone Tedeschi

12 accepted papers

2025

Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code

Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich +35
COLING 2025industry

Pretrained language models are integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim to democratize access to pretrained models for collaborative community development. Despite these efforts, such models enc…

Cited by 2Source ↗PDF ↗Scholar ↗
2025

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, Márton Kardos, Ashwin Mathur, David Stap +76
ICLR 2025poster

Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent this limitation and to provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) -- a large-scale community-dr…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models

Lorenzo Proietti, Stefano Perrella, Simone Tedeschi, Giulia Vulpis, Leonardo Lavalle, Andrea Sanchietti +2
COLING 2024main

Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP), aiming to assign the correct meaning (sense) to a word in context. However, traditional WSD systems rely on WordNet as the underlying sense inventory, often differentiating meticulously between subtle nuances of word…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2024

CNER: Concept and Named Entity Recognition

Giuliano Martinelli, Francesco Molfese, Simone Tedeschi, Alberte Fernández-Castro, Roberto Navigli
NAACL 2024long

Named entities – typically expressed via proper nouns – play a key role in Natural Language Processing, as their identification and comprehension are crucial in tasks such as Relation Extraction, Coreference Resolution and Question Answering, among others. Tasks like these also often entail dealing…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Language Pivoting from Parallel Corpora for Word Sense Disambiguation of Historical Languages: A Case Study on Latin

Iacopo Ghinassi, Simone Tedeschi, Paola Marongiu, Roberto Navigli, Barbara McGillivray
COLING 2024main

Word Sense Disambiguation (WSD) is an important task in NLP, which serves the purpose of automatically disambiguating a polysemous word with its most likely sense in context. Recent studies have advanced the state of the art in this task, but most of the work has been carried out on contemporary Eng…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2023

REDFM: a Filtered and Multilingual Relation Extraction Dataset

‪Pere-Lluís Huguet Cabot, Simone Tedeschi, Axel-Cyrille Ngonga Ngomo, Roberto Navigli
ACL 2023long

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. However, current RE models often rely on small datasets with low coverage of relation type…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗
2023

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

Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajič, Daniel Hershcovich, Eduard Hovy +6
ACL 2023long

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. Th…

Cited by 24Source ↗PDF ↗Scholar ↗
2022

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

Simone Tedeschi, Roberto Navigli
NAACL 2022findings

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. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a singl…

Cited by 48Source ↗PDF ↗code ↗Scholar ↗
2021

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

Simone Tedeschi, Simone Conia, Francesco Cecconi, Roberto Navigli
EMNLP 2021finding

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…

Cited by 52Source ↗PDF ↗code ↗Scholar ↗
2021

WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER

Simone Tedeschi, Valentino Maiorca, Niccolò Campolungo, Francesco Cecconi, Roberto Navigli
EMNLP 2021finding

Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multili…

Cited by 106Source ↗PDF ↗code ↗Scholar ↗