Semantic Scholar Open Access 2016 571 sitasi

Neural Text Generation from Structured Data with Application to the Biography Domain

R. Lebret David Grangier Michael Auli

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Abstrak

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. We experiment with a new dataset of biographies from Wikipedia that is an order of magnitude larger than existing resources with over 700k samples. The dataset is also vastly more diverse with a 400k vocabulary, compared to a few hundred words for Weathergov or Robocup. Our model builds upon recent work on conditional neural language model for text generation. To deal with the large vocabulary, we extend these models to mix a fixed vocabulary with copy actions that transfer sample-specific words from the input database to the generated output sentence. Our neural model significantly out-performs a classical Kneser-Ney language model adapted to this task by nearly 15 BLEU.

Topik & Kata Kunci

Computer Science

Penulis (3)

R. Lebret

David Grangier

Michael Auli

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APA MLA BibTeX

Lebret, R., Grangier, D., Auli, M. (2016). Neural Text Generation from Structured Data with Application to the Biography Domain. https://doi.org/10.18653/v1/D16-1128

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Lihat di Sumber doi.org/10.18653/v1/D16-1128

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 571×
Sumber Database: Semantic Scholar
DOI: 10.18653/v1/D16-1128
Akses: Open Access ✓