Text generation

By Michel Boyer, Guy Lapalme. 1990

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In Natural Language Processing

Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, background): “Data-to-Text Generation is the task of generating natural language descriptions conditioned on structured data [90, 127], such as tables [140, 207], database records [24], and knowledge graphs [54].”
BLEURT: Learning Robust Metrics for Text Generation (2020, background): “…tackle a wide array of tasks including translation (Koehn, 2009), summarization (Mani, 1999; Chopra et al., 2016), structured-data-to-text generation (McKeown, 1992; Kukich, 1983; Wiseman et al., 2017) dialog (Smith and Hipp, 1994; Vinyals and Le, 2015) and image cap-tioning (Fang et al., 2015).”
Controllable Abstractive Summarization (2017, background): “Text generation is an established research area (McKeown, 1992).”
ToTTo: A Controlled Table-To-Text Generation Dataset (2020, background): “Data-to-text generation (Kukich, 1983; McKeown, 1992) is the task of generating a target textual description y conditioned on source content x in the form of structured data such as a table.”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, methodology): “Early approaches, such as McKeown (1985), often relied on hand-crafted, domain-dependent structuring rules (which McKeown called schemata).”
Data-to-Text Generation with Content Selection and Planning (2018, methodology): “The generation literature provides multiple examples of content selection components developed for various domains which are either hand-built (Kukich 1983; McKeown 1992; Reiter and Dale 1997; Duboue and McKeown 2003) or learned from data (Barzilay and Lapata 2005; Duboue and McKeown 2001; 2003; Lia”
On Hallucination and Predictive Uncertainty in Conditional Language Generation (2021, unknown): “Data-to-text generation (Kukich, 1983; McKeown, 1992) is a task to generate textual content condi-”
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation (2019, background): “…(Mani, 1999), where x is a long document and y is a more concise version, machine translation (Koehn, 2009), where x and y represent equivalent text in different languages, and data-to-text generation (Kukich, 1983; McKeown, 1992), where x is a structured table and y is a textual description.”
Data-to-text Generation with Macro Planning (2021, methodology): “Traditional methods for data-to-text generation (Kukich, 1983; McKeown, 1992; Reiter and Dale, 1997) follow a pipeline architecture, adopting separate stages for text planning (determining which content to talk about and how it might be organized in discourse), sentence planning (aggregating content”
Generating Sequences by Learning to Self-Correct (2022, background): “Faithful constraint satisfaction is crucial for many downstream tasks, e.g., those that require converting information to text (McKeown, 1985).”
Explaining reasoning: an overview of explanation in knowledge-based systems (1991, background): “But research (McDonald, 1982; McKeown, 1985b) has resulted in generators with extensive grammars and explicit representations of English text.”
Text Generation with Exemplar-based Adaptive Decoding (2019, unknown): “, text summarization (Mani, 1999), machine translation (Koehn, 2009), and data-to-text generation (Kukich, 1983; McKeown, 1992; Reiter and Dale, 1997).”
Regeneration Learning: A Learning Paradigm for Data Generation (2023, background): “…of natural language processing (Manning and Schutze 1999; Collobert et al. 2011; Vaswani et al. 2017; Devlin et al. 2018; Brown 2009); X is style tag and Y is sentence in style-controlled text generation (McKeown 1992); X is text and Y is speech in text-to-speech synthesis (Tan et al. 2021, 2022).”