Recent pre-trained language models have shown promising capabilities in
generating fluent and realistic natural language text. However, generating
multi-sentence text with global content planning has been a long-existing
research question. Current approaches for controlled text generation can hardly
address this issue, as they usually condition on single known control
attributes. In this study, we propose a low-cost yet effective framework which
explicitly models the global content plan of the generated text. Specifically,
it optimizes the joint distribution of the natural language sequence and the
global content plan in a plug-and-play manner. We conduct extensive experiments
on the well-established Recipe1M+ benchmark. Both automatic and human
evaluations verify that our model achieves the state-of-the-art performance on
the task of recipe generation

本文提出了一种低成本但有效的框架，该框架以插拔的方式显式地对生成文本的全局内容计划建模，并证明其在食谱生成任务上实现了最新的技术性能，从而解决了当前受长期存在的多句文本生成问题。

基于内容计划的即插即用食谱生成

Plug-and-Play Recipe Generation with Content Planning

Generating texts which express complex ideas spanning multiple sentences
requires a structured representation of their content (document plan), but
these representations are prohibitively expensive to manually produce. In this
work, we address the problem of generating coherent multi-sentence texts from
the output of an information extraction system, and in particular a knowledge
graph. Graphical knowledge representations are ubiquitous in computing, but
pose a significant challenge for text generation techniques due to their
non-hierarchical nature, collapsing of long-distance dependencies, and
structural variety. We introduce a novel graph transforming encoder which can
leverage the relational structure of such knowledge graphs without imposing
linearization or hierarchical constraints. Incorporated into an encoder-decoder
setup, we provide an end-to-end trainable system for graph-to-text generation
that we apply to the domain of scientific text. Automatic and human evaluations
show that our technique produces more informative texts which exhibit better
document structure than competitive encoder-decoder methods.

本文介绍了一种新型图形转换编码器，可处理知识图谱等非层次性图形的内容，应用于科学文本领域的图 - 文本生成，评价结果表明，该技术可以产生比竞争的编码解码方法更具信息量且结构更好的文本。