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    <abstract>In this tutorial, we focus on text-to-text generation, a class ofnatural language generation (NLG) tasks, that takes a piece of text as inputand then generates a revision that is improved according to some specificcriteria (e.g., readability or linguistic styles), while largely retainingthe original meaning and the length of the text. This includes many usefulapplications, such as text simplification, paraphrase generation, styletransfer, etc. In contrast to text summarization and open-ended textcompletion (e.g., story), the text-to-text generation tasks we discuss inthis tutorial are more constrained in terms of semantic consistency andtargeted language styles. This level of control makes these tasks idealtestbeds for studying the ability of models to generate text that is bothsemantically adequate and stylistically appropriate. Moreover, these tasksare interesting from a technical standpoint, as they require complexcombinations of lexical and syntactical transformations, stylistic control,and adherence to factual knowledge, – all at once. With a special focus ontext simplification and revision, this tutorial aims to provide an overviewof the state-of-the-art natural language generation research from four majoraspects – Data, Models, Human-AI Collaboration, and Evaluation – and todiscuss and showcase a few significant and recent advances: (1) the use ofnon-retrogressive approaches; (2) the shift from fine-tuning to promptingwith large language models; (3) the development of new learnable metric andfine-grained human evaluation framework; (4) a growing body of studies anddatasets on non-English languages; (5) the rise of HCI+NLP+Accessibilityinterdisciplinary research to create real-world writing assistant systems.</abstract>
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%0 Conference Proceedings
%T Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision)
%A Dou, Yao
%A Laban, Philippe
%A Gardent, Claire
%A Xu, Wei
%Y Chiruzzo, Luis
%Y Lee, Hung-yi
%Y Ribeiro, Leonardo
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F dou-etal-2024-automatic
%X In this tutorial, we focus on text-to-text generation, a class ofnatural language generation (NLG) tasks, that takes a piece of text as inputand then generates a revision that is improved according to some specificcriteria (e.g., readability or linguistic styles), while largely retainingthe original meaning and the length of the text. This includes many usefulapplications, such as text simplification, paraphrase generation, styletransfer, etc. In contrast to text summarization and open-ended textcompletion (e.g., story), the text-to-text generation tasks we discuss inthis tutorial are more constrained in terms of semantic consistency andtargeted language styles. This level of control makes these tasks idealtestbeds for studying the ability of models to generate text that is bothsemantically adequate and stylistically appropriate. Moreover, these tasksare interesting from a technical standpoint, as they require complexcombinations of lexical and syntactical transformations, stylistic control,and adherence to factual knowledge, – all at once. With a special focus ontext simplification and revision, this tutorial aims to provide an overviewof the state-of-the-art natural language generation research from four majoraspects – Data, Models, Human-AI Collaboration, and Evaluation – and todiscuss and showcase a few significant and recent advances: (1) the use ofnon-retrogressive approaches; (2) the shift from fine-tuning to promptingwith large language models; (3) the development of new learnable metric andfine-grained human evaluation framework; (4) a growing body of studies anddatasets on non-English languages; (5) the rise of HCI+NLP+Accessibilityinterdisciplinary research to create real-world writing assistant systems.
%U https://aclanthology.org/2024.acl-tutorials.2
%P 3-4

Markdown (Informal)

[Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision)](https://aclanthology.org/2024.acl-tutorials.2) (Dou et al., ACL 2024)

• Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (Dou et al., ACL 2024)

ACL

• Yao Dou, Philippe Laban, Claire Gardent, and Wei Xu. 2024. Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts), pages 3–4, Bangkok, Thailand. Association for Computational Linguistics.