Controllable Text Generation (CTG) is an emerging area in the field of natural language
generation (NLG). It is regarded as crucial for the development of advanced text generation …

Deep generative models have been an upsurge in the deep learning community since they
were proposed. These models are designed for generating new synthetic data including …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

Event extraction is challenging due to the complex structure of event records and the
semantic gap between text and event. Traditional methods usually extract event records by …

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …

Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper,
we investigate two recently proposed pretrained language models (PLMs) and analyze the …

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 …

Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-
tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which …

In recent years, significant progress has been made in text generation. The latest text
generation models are revolutionizing the domain by generating human-like text. It has …