Related Papers
RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation
AbstractBy addressing both text-to-AMR parsing and AMR-to-text generation, SemEval-2017 Task 9 established AMR as a powerful semantic interlingua. We strengthen the interlingual aspect of AMR by applying the multilingual Grammatical …...
The State of the Art in Semantic Representation
AbstractSemantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little …...
Broad-Coverage Semantic Parsing as Transduction
AbstractWe unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By …...
AMR Similarity Metrics from Principles
AbstractDifferent metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. …...
CLEVE: Contrastive Pre-training for Event Extraction
AbstractEvent extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot …...
QASem Parsing: Text-to-text Modeling of QA-based Semantics
AbstractVarious works suggest the appeals of incorporating explicit semantic representations when addressing challenging realistic NLP scenarios. Common approaches offer either comprehensive linguistically-based formalisms, like AMR, or alternatively Open-IE, which provides …...
Evaluate AMR Graph Similarity via Self-supervised Learning
AbstractIn work on AMR (Abstract Meaning Representation), similarity metrics are crucial as they are used to evaluate AMR systems such as AMR parsers. Current AMR metrics are all based on …...