Automatic and Human-AI Interactive Text Generation
Yao Dou Philippe Laban Claire Gardent Wei Xu
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.