1 code implementation • 23 Jun 2023 • Pavlo Vasylenko, Pere-Lluís Huguet Cabot, Abelardo Carlos Martínez Lorenzo, Roberto Navigli
Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text.
Ranked #3 on AMR Parsing on LDC2020T02 (using extra training data)
1 code implementation • 19 Jun 2023 • 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.
1 code implementation • 16 Jun 2023 • Pere-Lluís Huguet Cabot, Simone Tedeschi, Axel-Cyrille Ngonga Ngomo, Roberto Navigli
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 language and structured knowledge.
1 code implementation • 15 Jun 2022 • 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 of different languages.