The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

The dominant paradigm for neural text generation is left-to-right decoding from
autoregressive language models. Constrained or controllable generation under complex …

Dialogue systems have attracted more and more attention. Recent advances on dialogue
systems are overwhelmingly contributed by deep learning techniques, which have been …

Sequence to sequence learning models still require several days to reach state of the art
performance on large benchmark datasets using a single machine. This paper shows that …

This paper surveys the current state of the art in Natural Language Generation (NLG),
defined as the task of generating text or speech from non-linguistic input. A survey of NLG is …

In this paper, we survey the methods and concepts developed for the evaluation of dialogue
systems. Evaluation, in and of itself, is a crucial part during the development process. Often …

The majority of NLG evaluation relies on automatic metrics, such as BLEU. In this paper, we
motivate the need for novel, system-and data-independent automatic evaluation methods …

This paper describes the E2E data, a new dataset for training end-to-end, data-driven
natural language generation systems in the restaurant domain, which is ten times bigger …