Papers by Simone Tedeschi
How Much Do Encoder Models Know About Word Senses? (2025.acl-long)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP) however, how well these models inherently disambiguate word senses remains uncertain. |
| Approach: | They evaluate several encoder-only PLMs across WordNet and ODE sense inventories to evaluate their ability to separate word senses without any task-specific fine-tuning. |
| Outcome: | The proposed model outperforms output layer on WordNet and ODE sense inventories by 15 percentage points. |
Language Pivoting from Parallel Corpora for Word Sense Disambiguation of Historical Languages: A Case Study on Latin (2024.lrec-main)
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| Challenge: | Word Sense Disambiguation (WSD) is an important task in NLP . most of the work on this task has been done on contemporary English or other modern languages, leaving challenges posed by low-resource languages and diachronic change open. |
| Approach: | They propose to use existing bilingual corpora instead of native English datasets to generate a Latin WSD model. |
| Outcome: | The proposed approach achieves state-of-the-art on a standard benchmark for Latin WSD. |
Neuralign: A Context-Aware, Cross-Lingual and Fully-Neural Sentence Alignment System for Long Texts (2024.eacl-long)
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| Challenge: | Existing sentence alignment systems focus on auxiliary information such as document metadata and hyperparameter-sensitive techniques, and neglect the crucial role that context plays in the alignment process. |
| Approach: | They propose a context-aware, end-to-end and fully-neural architecture for sentence alignment that maps source and target sentences in long documents by contextualizing their sentence embeddings with respect to the other sentences in the document. |
| Outcome: | The proposed system maps source and target sentences in long documents by contextualizing their sentence embeddings with respect to the other sentences in the document. |
Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models (2024.lrec-main)
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Lorenzo Proietti, Stefano Perrella, Simone Tedeschi, Giulia Vulpis, Leonardo Lavalle, Andrea Sanchietti, Andrea Ferrari, Roberto Navigli
| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP) but current pretrained language models lack the granularity to perform disambiguation . |
| Approach: | They propose a large-scale resource that leverages homonymy relations to cluster WordNet senses and train Homonymy Disambiguation systems. |
| Outcome: | The proposed model can distinguish homonyms with up to 95% accuracy even without fine-tuning the underlying PLM. |
CNER: Concept and Named Entity Recognition (2024.naacl-long)
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| Challenge: | Concept and Named Entity Recognition (CNER) is a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Approach: | They propose a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Outcome: | The proposed task gains +5.4 and +8 macro F1 points when performed as a unified task compared to specialized named entity and concept recognition systems. |
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code (2025.coling-industry)
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Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Barbosa Junior, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Nour Moustafa-Fahmy, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Hiep Nguyen, Sampo Pyysalo
| Challenge: | Pretrained language models are integral part of AI applications, but their high computational cost limits accessibility. |
| Approach: | They evaluate Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. |
| Outcome: | The proposed model outperforms existing models on English, Finnish, Hindi, Japanese, Vietnamese, and code. |
ID10M: Idiom Identification in 10 Languages (2022.findings-naacl)
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| Challenge: | Identifying and understanding idioms in context is a key goal and challenge in Natural Language Understanding tasks. |
| Approach: | They propose a multilingual Transformer-based system for the identification of idioms and a manually-curated evaluation benchmark. |
| Outcome: | The proposed system performs well in 10 languages and is released on github. |
What’s the Meaning of Superhuman Performance in Today’s NLU? (2023.acl-long)
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Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajič, Daniel Hershcovich, Eduard Hovy, Alexander Koller, Simon Krek, Steven Schockaert, Rico Sennrich, Ekaterina Shutova, Roberto Navigli
| Challenge: | Recent research has focused on developing larger pretrained language models and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities. |
| Approach: | They propose to use benchmarks such as SuperGLUE and SQUAD to evaluate PLMs' abilities in language understanding, reasoning, and reading comprehension to assess their performance. |
| Outcome: | The proposed benchmarks have serious limitations affecting comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks. |
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER (2021.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a key intermediate task in NLP. |
| Approach: | They propose a method which uses knowledge-based approaches and neural models to produce high-quality training corpora for NER. |
| Outcome: | The proposed method improves on standard benchmarks and yields significant improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation. |
Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
| Outcome: | The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. |
REDFM: a Filtered and Multilingual Relation Extraction Dataset (2023.acl-long)
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| Challenge: | Existing Relation Extraction models rely on small datasets with low coverage of relation types . current systems rely only on small data sets with limited coverage of relationship types - especially when working with languages other than english. |
| Approach: | They propose to use an automatic annotated dataset to train relation extraction systems. |
| Outcome: | The proposed model can extract triplets in multiple languages from a human-revised dataset. |
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation) (2022.findings-naacl)
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| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
| Approach: | They propose a method for automatically producing NER annotations and introduce a manually-annotated test set. |
| Outcome: | The proposed method covers 10 languages, 15 NER categories and 2 textual genres and a manually-annotated test set. |