In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines
integrating several different modules or components, and exploit graph recategorization, ie …

Although neural models have achieved competitive results in dialogue systems, they have
shown limited ability in representing core semantics, such as ignoring important entities. To …

Meaning Representation (AMR) is a popular formalism of natural language that represents
the meaning of a sentence as a semantic graph. It is agnostic about how to derive meanings …

Meaning Representation (AMR) is a representation model in which AMRs are rooted and
labeled graphs that capture semantics on the sentence level while abstracting away from …

A language-independent representation of meaning is one of the most coveted dreams in
Natural Language Understanding. With this goal in mind, several formalisms have been …

The generation of logically coherent dialogues by humans relies on underlying cognitive
abilities. Based on this, we redefine the dialogue coherence evaluation process, combining …

Abstract Although parsing to Abstract Meaning Representation (AMR) has become very
popular and AMR has been shown effective on the many sentence-level downstream tasks …

Abstract Semantics, the study of meaning, is central to research in Natural Language
Processing (NLP) and many other fields connected to Artificial Intelligence. Nevertheless …

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 …

Graph-based semantic parsing aims to represent textual meaning through directed graphs.
As one of the most promising general-purpose meaning representations, these structures …