Natural language generation: The commercial state of the art in 2020

By Rick Dale. 2020

57 citations recorded by the source corpus.

Abstract It took a while, but natural language generation is now an established commercial software category. It’s commented upon frequently in both industry media and the mainstream press, and businesses are willing to pay hard cash to take advantage of the technology. We look at who’s active in the space, the nature of the technology that’s available today and where things might go in the future.

Subfields: Artificial Intelligence, Information Systems, Management Information Systems, Natural Language Processing

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Citations in the corpus

Structure-to-Text Generation with Self-Training, Acceptability Classifiers and Context-Conditioning for the GEM Shared Task (2021, background): “, 2019) have become popular for their data-driven scaling story and superior naturalness over typical template-based systems (Gatt and Krahmer, 2018; Dale, 2020).”
Getting to Production with Few-shot Natural Language Generation Models (2021, methodology): “Commercial NLG systems are typically built on rule- or template-based text generation methods (Reiter and Dale, 2000; Gatt and Krahmer, 2018; Dale, 2020).”
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020, methodology): “NLG systems employed in commercial settings are typically based on template-based text generation techniques (Reiter and Dale, 2000; Gatt and Krahmer, 2018; Dale, 2020).”