4 papers
Language Models Can Resolve Reference Compositionally, But It's Not Their Native Strength: The Case of the Personal Relation Task
Bart Evelo, Meaghan Fowlie, Denis Paperno
Do neural models, such as Large Language Models, genuinely acquire compositional abilities for interpretation of natural language? When we talk about semantic interpretation, we ca…
AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite
Jonas Groschwitz, Shay B. Cohen, Lucia Donatelli +1
We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics. AMR parsers n…
Learning compositional structures for semantic graph parsing
Jonas Groschwitz, Meaghan Fowlie, Alexander Koller
AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accu…
AMR Dependency Parsing with a Typed Semantic Algebra
Jonas Groschwitz, Matthias Lindemann, Meaghan Fowlie +2
We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allow…