Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …

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 …

A survey of computational methods for understanding, generating, and manipulating human
language, which offers a synthesis of classical representations and algorithms with …

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 …

There is considerable interest in the task of automatically generating image captions.
However, evaluation is challenging. Existing automatic evaluation metrics are primarily …

The rapid progress of Natural Language Processing (NLP) technologies has led to the
widespread availability and effectiveness of text generation tools such as ChatGPT and …

Many NLP applications can be framed as a graph-to-sequence learning problem. Previous
work proposing neural architectures on this setting obtained promising results compared to …

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 …

Scene graphs are powerful representations that parse images into their abstract semantic
elements, ie, objects and their interactions, which facilitates visual comprehension and …

Large language models have recently achieved state of the art performance across a wide
variety of natural language tasks. Meanwhile, the size of these models and their latency …