natural-language-processing-5
Data-to-Text Generation
3260 papers • 126 benchmarks • 313 datasets
A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation. ( Image credit: Data-to-Text Generation with Content Selection and Planning )
(Image credit: Papersgraph)
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These leaderboards are used to track progress in data-to-text-generation-5
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WebNLG
WebNLG
E2E NLG Challenge
E2E NLG Challenge
WebNLG Full
WebNLG Full
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iUse these libraries to find data-to-text-generation-5 models and implementations
2 papers 201
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Challenges in Data-to-Document Generation
Sam Wiseman, Alexander M. Rush, Stuart M. Shieber•Fri Jun 30 2017
A new, large-scale corpus of data records paired with descriptive documents is introduced, a series of extractive evaluation methods for analyzing performance are proposed, and baseline results are obtained using current neural generation methods.
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