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

Empirical Methods in Natural Language Processing (EMNLP)


This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. It generates biographical sentences from fact tables on a new dataset of biographies from Wikipedia. This set is an order of magnitude larger than existing resources with over 700k samples and a 400k vocabulary. Our model builds on conditional neural language models 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. To deal with structured data, we allow the model to embed words differently depending on the data fields in which they occur. Our neural model significantly outperforms a Templated Kneser-Ney language model by nearly 15 BLEU.

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