Papers by Vincenzo Deufemia

1 papers
MIMIC: Multi-party Dialogue Augmentation via Speaker Stylistic Transfer (2026.findings-eacl)

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Challenge: Existing discourse annotations are limited and annotated data scarcity has hindered progress in discourse parsing.
Approach: They propose a framework for augmenting discourse-annotated corpora via speaker stylistic transfer using Large Language Models (LLMs).
Outcome: The proposed framework outperforms parsers trained on STAC and Molweni corpora on a multi-party dialogue with consistent gains for underrepresented discourse patterns and in low-resource scenarios.

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