Papers by Federico Martelli
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. |
Do Large Language Models Understand Word Senses? (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have set new performance standards in a wide range of tasks. |
| Approach: | They evaluate the Word Sense Disambiguation capabilities of instruction-tuned LLMs and their ability to understand word senses in three generative settings: definition generation, free-form explanation, and example generation. |
| Outcome: | The proposed models can explain the meaning of words in context with 98% accuracy, while demonstrating greater robustness across domains and levels of difficulty. |
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation (2022.acl-long)
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| Challenge: | Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. |
| Approach: | They propose a new benchmark to study semantic biases in Machine Translation of nominal and verbal words in five different languages. |
| Outcome: | The proposed benchmark tests state-of-the-art machine translation systems against the new test bed and provides a statistical and linguistic analysis of the results. |
SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations (D19-1)
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| Challenge: | Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed. |
| Approach: | They propose to use a Lexical Knowledge Base to capture syntagmatic relations to enable knowledge-based WSD systems to achieve a new state of the art. |
| Outcome: | The proposed resource captures syntagmatic relations and is the first large-scale manually-curated resource of this kind made available to the community. |