In this tutorial, we focus on text-to-text generation, a class of natural
language generation (NLG) tasks, that takes a piece of text as input and then
generates a revision that is improved according to some specific criteria
(e.g., readability or linguistic styles), while largely retaining the original
meaning and the length of the text. This includes many useful applications,
such as text simplification, paraphrase generation, style transfer, etc. In
contrast to text summarization and open-ended text completion (e.g., story),
the text-to-text generation tasks we discuss in this tutorial are more
constrained in terms of semantic consistency and targeted language styles. This
level of control makes these tasks ideal testbeds for studying the ability of
models to generate text that is both semantically adequate and stylistically
appropriate. Moreover, these tasks are interesting from a technical standpoint,
as they require complex combinations of lexical and syntactical
transformations, stylistic control, and adherence to factual knowledge, -- all
at once. With a special focus on text simplification and revision, this
tutorial aims to provide an overview of the state-of-the-art natural language
generation research from four major aspects -- Data, Models, Human-AI
Collaboration, and Evaluation -- and to discuss and showcase a few significant
and recent advances: (1) the use of non-retrogressive approaches; (2) the shift
from fine-tuning to prompting with large language models; (3) the development
of new learnable metric and fine-grained human evaluation framework; (4) a
growing body of studies and datasets on non-English languages; (5) the rise of
HCI+NLP+Accessibility interdisciplinary research to create real-world writing
assistant systems.

本教程关注于文本生成，这是一类自然语言生成任务，它以一段文本作为输入，然后生成一篇按照某些特定标准（如可读性或语言风格）改进的修订版本，同时保留原版文本的大部分含义和长度。

自动人工智能互动文本生成

Automatic and Human-AI Interactive Text Generation

Natural Language Generation (NLG) for task-oriented dialogue systems focuses
on communicating specific content accurately, fluently, and coherently. While
these attributes are crucial for a successful dialogue, it is also desirable to
simultaneously accomplish specific stylistic goals, such as response length,
point-of-view, descriptiveness, sentiment, formality, and empathy. In this
work, we focus on stylistic control and evaluation for schema-guided NLG, with
joint goals of achieving both semantic and stylistic control. We experiment in
detail with various controlled generation methods for large pretrained language
models: specifically, conditional training, guided fine-tuning, and guided
decoding. We discuss their advantages and limitations, and evaluate them with a
broad range of automatic and human evaluation metrics. Our results show that
while high style accuracy and semantic correctness are easier to achieve for
more lexically-defined styles with conditional training, stylistic control is
also achievable for more semantically complex styles using discriminator-based
guided decoding methods. The results also suggest that methods that are more
scalable (with less hyper-parameters tuning) and that disentangle content
generation and stylistic variations are more effective at achieving semantic
correctness and style accuracy.

本研究着重于架构引导的自然语言生成中的风格控制和评估，旨在同时实现语义和风格控制，通过条件训练、引导微调和引导解码等各种控制生成方法的实验来评估其优缺点，并用广泛的自动和人工评估指标来评估，结果表明，使用判别器引导解码更适合处理风格较为语义复杂的任务，同时这种方法也更加可扩展，效果更好。

基于模式引导的自然语言生成的风格控制

Style Control for Schema-Guided Natural Language Generation

Recent neural approaches to data-to-text generation have mostly focused on
improving content fidelity while lacking explicit control over writing styles
(e.g., word choices, sentence structures). More traditional systems use
templates to determine the realization of text. Yet manual or automatic
construction of high-quality templates is difficult, and a template acting as
hard constraints could harm content fidelity when it does not match the record
perfectly. We study a new way of stylistic control by using existing sentences
as soft templates. That is, the model learns to imitate the writing style of
any given exemplar sentence, with automatic adaptions to faithfully describe
the content record. The problem is challenging due to the lack of parallel
data. We develop a neural approach that includes a hybrid attention-copy
mechanism, learns with weak supervisions, and is enhanced with a new content
coverage constraint. We conduct experiments in restaurants and sports domains.
Results show our approach achieves stronger performance than a range of
comparison methods. Our approach balances well between content fidelity and
style control given exemplars that match the records to varying degrees.

研究了一种新的文体控制方法，使用现有的句子作为软模板，包括一种混合注意力 - 复制机制、弱监督学习及新的内容覆盖约束，结果表明该方法在餐馆和体育领域表现更好，在内容保真度和文体控制之间取得了较好的平衡。