Papers by Abelardo Carlos Martinez Lorenzo
MOSAICo: a Multilingual Open-text Semantically Annotated Interlinked Corpus (2024.naacl-long)
Copied to clipboard
Simone Conia, Edoardo Barba, Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Riccardo Orlando, Luigi Procopio, Roberto Navigli
| Challenge: | Existing approaches to integrate semantics into Natural Language Understanding (NLP) systems are cost-effective and environmental impact-related. |
| Approach: | They propose to provide semantically-annotated corpora for four NLU tasks across five languages and to drop the requirement of closed datasets. |
| Outcome: | The proposed model provides hundreds of millions of silver yet high-quality annotations for four NLU tasks across five languages. |
Efficient AMR Parsing with CLAP: Compact Linearization with an Adaptable Parser (2024.lrec-main)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) parsers face efficiency challenges because of their large model size and computational time, which limit their accessibility within the research community. |
| Approach: | They propose a novel linearization system that simplifies encoding and reduces the number of tokens by between 40% and 50%. |
| Outcome: | The proposed system reduces the number of tokens by 40% and 50% while maintaining high performance while reducing training and inference times. |
Mitigating Data Scarcity in Semantic Parsing across Languages with the Multilingual Semantic Layer and its Dataset (2024.findings-acl)
Copied to clipboard
Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Karim Ghonim, Lu Xu, Hee-Soo Choi, Alberte Fernández-Castro, Roberto Navigli
| Challenge: | Large Language Models (LLMs) have advanced significantly in understanding human text, but semantic representations remain crucial for various applications. |
| Approach: | They introduce a multilingual semantic layer which decouples from disambiguation and external inventories and simplifies the task. |
| Outcome: | The proposed model reduces performance gap between languages and annotators by enabling them to understand semantic relations between concepts in any language. |