Abstract meaning representation (AMR) highlights the core semantic information of text in a
graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of …

Knowledge base question answering (KBQA) is an important task in Natural Language
Processing. Existing approaches face significant challenges including complex question …

Abstract The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation
(AMR), share similar goals with Information Extraction (IE) to convert natural language texts …

Abstract We introduce Transformer Grammars (TGs), a novel class of Transformer language
models that combine (i) the expressive power, scalability, and strong performance of …

Abstract The 2020 Shared Task at the Conference for Computational Language Learning
(CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and …

Most previous studies aim at extracting events from a single sentence, while document-level
event extraction still remains under-explored. In this paper, we focus on extracting event …

Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained
sequence-to-sequence Transformer models has recently led to large improvements on AMR …

Meaning Representation parsing is a sentence-to-graph prediction task where target nodes
are not explicitly aligned to sentence tokens. However, since graph nodes are semantically …

This is a demonstration of our newly released Python package NL2LTL which leverages the
latest in natural language understanding (NLU) and large language models (LLMs) to …

AMR parsing has experienced an unprecendented increase in performance in the last three
years, due to a mixture of effects including architecture improvements and transfer learning …