activity
20212024
most citedCross-lingual AMR Aligner: Paying Attention to Cross-Attention

2 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2024★ 1 cited

Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics

Stefano Perrella, Lorenzo Proietti, Pere-Lluís Huguet Cabot +2

Machine Translation (MT) evaluation metrics assess translation quality automatically. Recently, researchers have employed MT metrics for various new use cases, such as data filteri…

cs.CL2024

ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget

Riccardo Orlando, Pere-Lluis Huguet Cabot, Edoardo Barba +1

Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper…

cs.CL2023

Incorporating Graph Information in Transformer-based AMR Parsing

Pavlo Vasylenko, Pere-Lluís Huguet Cabot, Abelardo Carlos Martínez Lorenzo +1

Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text. Current approaches are based on…

cs.CL2023★ 1 cited

AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing

Abelardo Carlos Martínez Lorenzo, Pere-Lluís Huguet Cabot, Roberto Navigli

In this paper, we examine the current state-of-the-art in AMR parsing, which relies on ensemble strategies by merging multiple graph predictions. Our analysis reveals that the pres…

cs.CL2023

RED: a Filtered and Multilingual Relation Extraction Dataset

Pere-Lluís Huguet Cabot, Simone Tedeschi, Axel-Cyrille Ngonga Ngomo +1

Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural langu…

cs.CL2022★ 2 cited

Cross-lingual AMR Aligner: Paying Attention to Cross-Attention

Abelardo Carlos Martínez Lorenzo, Pere-Lluís Huguet Cabot, Roberto Navigli

This paper introduces a novel aligner for Abstract Meaning Representation (AMR) graphs that can scale cross-lingually, and is thus capable of aligning units and spans in sentences…