Papers by Giorgio Satta
Sequence-to-sequence Models for Cache Transition Systems (P18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism where the meaning of a sentence is encoded as a rooted, directed graph. |
| Approach: | They propose a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs using a special transition system called a cache transition system. |
| Outcome: | The proposed model outperforms other sequence-to-sequence approaches and achieves competitive results in comparison with the best-performing models. |
Detecting Winning Arguments with Large Language Models and Persuasion Strategies (2026.findings-eacl)
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| Challenge: | Recent studies have focused on predicting winning arguments, i.e., those that effectively convince a reader to adopt a certain opinion. |
| Approach: | They propose to use large language models with a chain-of-thought framework to guide reasoning over six persuasion strategies to determine persuasiveness. |
| Outcome: | The proposed approach leverages large language models with a chain-of-thought framework that guides reasoning over six persuasion strategies. |