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 …

Constituency parsing and nested named entity recognition (NER) are similar tasks since
they both aim to predict a collection of nested and non-crossing spans. In this work, we cast …

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

Automatic evaluation metrics are essential for the rapid development of open-domain
dialogue systems as they facilitate hyper-parameter tuning and comparison between …

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 …

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 …

Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word
alignments. These alignments are learned separately from parser training and require a …

Implicit event argument extraction (EAE) aims to identify arguments that could scatter over
the document. Most previous work focuses on learning the direct relations between …

In many machine learning tasks, models are trained to predict structure data such as graphs.
For example, in natural language processing, it is very common to parse texts into …

AMR parsing is the task that maps a sentence to an AMR semantic graph automatically. The
difficulty comes from generating the complex graph structure. The previous state-of-the-art …