It has always been an important yet challenging problem to control langu...
Non-AutoRegressive (NAR) text generation models have drawn much attentio...
The standard paradigm of neural language generation adopts maximum likel...
While the adoption of Service-Oriented Architectures (SOA) eases the
imp...
Training language models to learn from human instructions for zero-shot
...
Despite the success of text-to-text pre-trained models in various natura...
Existing reference-free metrics have obvious limitations for evaluating
...
Large-scale pre-training has shown remarkable performance in building
op...
Distinct is a widely used automatic metric for evaluating the diversity ...
Although pre-trained language models have remarkably enhanced the genera...
Existing pre-trained models for knowledge-graph-to-text (KG-to-text)
gen...
Pre-trained Language Models (PLMs) have proven to be beneficial for vari...
Commonsense explanation generation aims to empower the machine's sense-m...
Despite the success of generative pre-trained language models on a serie...
The advancements of neural dialogue generation models show promising res...
In text generation evaluation, many practical issues, such as inconsiste...
Most of the existing pre-trained language representation models neglect ...
Most of the existing generative adversarial networks (GAN) for text
gene...