Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

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

Abstract The 2019 Shared Task at the Conference for Computational Language Learning
(CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five …

We unify different broad-coverage semantic parsing tasks under a transduction paradigm,
and propose an attention-based neural framework that incrementally builds a meaning …

Semantic representation is a way of expressing the meaning of a text that can be processed
by a machine to serve a particular natural language processing (NLP) task that usually …

Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite …

We present the SemEval 2019 shared task on UCCA parsing in English, German and
French, and discuss the participating systems and results. UCCA is a cross-linguistically …

We introduce a transductive model for parsing into Universal Decompositional Semantics
(UDS) representations, which jointly learns to map natural language utterances into UDS …

Symbolic meaning representations of natural language text have been studied since at least
the 1960s. With the availability of large annotated corpora, and more powerful machine …

Saarland at MRP 2019: Compositional parsing across all graphbanks Page 1 Proceedings of
the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 CoNLL …