Papers by Irene Sucameli
Representing Verbs with Visual Argument Vectors (2020.lrec-1)
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| Challenge: | Existing models for verb semantic similarities are based on linguistic data, but they do not register intuitive attributes. |
| Approach: | They evaluated two textual distributional semantic models and a visual one to explore verb semantic similarities. |
| Outcome: | The proposed models extract meaningful information and capture semantic similarity between verbs using visual distributional models. |
MATILDA - Multi-AnnoTator multi-language InteractiveLight-weight Dialogue Annotator (2021.eacl-demos)
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| Challenge: | MATILDA is the first multi-annotator, multi-language dialogue annotation tool . it allows the creation of corpora, the management of users, the annotation of dialogues, the quick adaptation of the user interface to any language and the resolution of interannotation disagreement. |
| Approach: | They propose to use MATILDA to create corpora, manage users, and annotation dialogues. |
| Outcome: | The proposed tool supports the full pipeline for dialogue annotation, and non-technical people can use it. |
ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models (2025.findings-acl)
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Martina Miliani, Serena Auriemma, Alessandro Bondielli, Emmanuele Chersoni, Lucia Passaro, Irene Sucameli, Alessandro Lenci
| Challenge: | Large Language Models (LLMs) are increasingly used in tasks requiring interpretive and inferential accuracy. |
| Approach: | They propose a dataset for evaluating Large Language Models in explicit causal reasoning that integrates causal and temporal relations presented in different linguistic orders and explicitly expressed by linguistic connectives. |
| Outcome: | The proposed model performs better than existing models in the domain of causal reasoning. |