This article offers a comprehensive review of the research on Natural Language Generation
(NLG) over the past two decades, especially in relation to data-to-text generation and text-to …

The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent
responses for many downstream tasks, eg, task-oriented dialog and question answering …

We study knowledge-grounded dialogue generation with pre-trained language models. To
leverage the redundant external knowledge under capacity constraint, we propose …

There is a resurgent interest in developing intelligent open-domain dialog systems due to
the availability of large amounts of conversational data and the recent progress on neural …

Conversational recommender systems (CRS) aim to recommend high-quality items to users
through interactive conversations. To develop an effective CRS, the support of high-quality …

Recently, retrieval-augmented text generation attracted increasing attention of the
computational linguistics community. Compared with conventional generation models …

Knowledge-grounded dialogue systems are intended to convey information that is based on
evidence provided in a given source text. We discuss the challenges of training a generative …

Recently retrieval-augmented text generation has achieved state-of-the-art performance in
many NLP tasks and has attracted increasing attention of the NLP and IR community, this …

Knowledge-grounded dialogue is a task of generating an informative response based on
both discourse context and external knowledge. As we focus on better modeling the …