Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

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 …

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task.
Many recent works have used dependency trees to extract the relation between aspects and …

Pretrained language models (PLM) have recently advanced graph-to-text generation, where
the input graph is linearized into a sequence and fed into the PLM to obtain its …

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 …

Logical table-to-text generation aims to automatically generate fluent and logically faithful
text from tables. The task remains challenging where deep learning models often generated …

Generating text from structured inputs, such as meaning representations or RDF triples, has
often involved the use of specialized graph-encoding neural networks. However, recent …

Abstract meaning representation (AMR) is a graph-based sentence-level meaning
representation that has become highly popular in recent years. AMR is a knowledge-based …

Pre-trained language models have made great progress on dialogue tasks. However, these
models are typically trained on surface dialogue text, thus are proven to be weak in …

The neural boom that has sparked natural language processing (NLP) research throughout
the last decade has similarly led to significant innovations in data-to-text generation (D2T) …