Papers by Luigi Di Caro

5 papers
Building Semantic Grams of Human Knowledge (2020.lrec-1)

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Challenge: Word senses are typically defined with textual definitions and put in context via lexical-semantic relations such as synonymy, antonymy, hypernymy, etc.
Approach: They propose a slot-filler structure to define the meaning of words in terms of their prototypical semantic information.
Outcome: The proposed model improves on a semantic similarity task and shows significant improvements over state-of-the-art embeddings.
Populating Legal Ontologies using Semantic Role Labeling (2020.lrec-1)

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Challenge: This paper is concerned with the ‘resource consumption bottleneck’ of creating semantic technologies manually.
Approach: They propose to combine general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge to solve the acquisition paradox.
Outcome: The proposed system extracts norms from legislation and represents them as structured norms in legal ontologies.
Eta-WavLM: Efficient Speaker Identity Removal in Self-Supervised Speech Representations Using a Simple Linear Equation (2025.findings-acl)

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Challenge: Existing methods for learning meaningful representations from unannotated data are resource-intensive and degrade other speech components.
Approach: They propose a method that decomposes SSL representations into speaker-specific components and generates speaker disentangled representations.
Outcome: The proposed method achieves speaker independence and improves on state-of-the-art methods.
EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detection (2024.lrec-main)

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Challenge: EcoVerse is an annotated English Twitter dataset of 3,023 tweets . mainstream NLP tasks dominate the scene, but environmental impacts remain unstudied .
Approach: They propose an annotation scheme for Eco-Relevance Classification, Stance Detection and an original approach for Environmental Impact Analysis.
Outcome: The proposed scheme produces consistent annotations of high quality . the dataset is made freely available to stimulate further research .
Enhancing Polyglot Voices by Leveraging Cross-Lingual Fine-Tuning in Any-to-One Voice Conversion (2024.findings-emnlp)

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Challenge: Recent advances in speech synthesis have improved the quality of polyglot voices.
Approach: They propose a cross-lingual any-to-one voice conversion system that preserves the source accent without multilingual data from the target speaker.
Outcome: The proposed system preserves source accent without multilingual data from target speaker and reduces training data requirements.

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