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Found 4 papers, 4 papers with code
Incorporating Graph Information in Transformer-based AMR Parsing
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)
AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing
In this paper, we examine the current state-of-the-art in AMR parsing, which relies on ensemble strategies by merging multiple graph predictions.
RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset
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.
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention
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.
