We investigate the generation of onesentence Wikipedia biographies from facts derived from Wikidata slot-value pairs . We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries . Our model incorporates a novel secondary objective that helps ensure it generates sentences that contain the input facts . The model achieves a BLEU score of 41 , improving significantly upon the vanilla sequence-to-sequence model and scoring roughly twice that of a simple template baseline . Human preference evaluation suggests the model is nearly as good as the Wikipedia reference . Manual analysis explores content selection , suggesting the model can trade the ability to infer knowledge against the risk of hallucinating incorrect information . Despite massive effort , Wikipedia and other collaborative knowledge bases ( KBs ) have coverage and quality problems . Popular topics are covered in great detail , but there is a long tail of specialist topics with little or no text . Other text can be incorrect , whether by accident or vandalism . We report on the task of generating textual summaries for people , mapping slot-value facts to onesentence encyclopaedic biographies . In addition to initialising stub articles with only structured data , the resulting model could be used to improve consistency and accuracy of existing articles . Figure 1 shows a Wikidata entry for Mathias Tuomi , with fact keys and values flattened into a sequence , and the first sentence from his Wikipedia article . Some values are in the text , others are missing TITLE mathias tuomi SEX OR GENDER male DATE OF BIRTH 1985 - 09 - 03 OCCUPATION squash player CITIZENSHIP finland ( e.g. male ) or expressed differently ( e.g. dates ) . We treat this knowlege-to-text task like translation , using a recurrent neural network ( RNN ) sequence-to-sequence model ( Sutskever et al . , 2014 ) that learns to select and realise the most salient facts as text . This includes an attention mechanism to focus generation on specific facts , a shared vocabulary over input and output , and a multi-task autoencoding objective for the complementary extraction task . We create a reference dataset comprising more than 400,000 knowledgetext pairs , handling the 15 most frequent slots . We also describe a simple template baseline for comparison on BLEU and crowd-sourced human preference judgements over a heldout TEST set . Our model obtains a BLEU score of 41.0 , compared to 33.1 without the autoencoder and 21.1 for the template baseline . In a crowdsourced preference evaluation , the model outperforms the baseline and is preferred 40 % of the time to the Wikipedia reference . Manual analysis of content selection suggests that the model can infer knowledge but also makes mistakes , and that the autoencoding objective encourages the model to select more facts without increasing sentence length . The task formulation and models are a foundation for text completion and consistency in KBs . We present a neural model for mapping between structured and unstructured data , focusing on creating Wikipedia biographic summary sentences from Wikidata slot-value pairs . We introduce a sequence-to-sequence autoencoding RNN which improves upon base models by jointly learning to generate text and reconstruct facts . Our analysis of the task suggests evaluation in this domain is challenging . In place of a single score , we analyse statistical measures , human preference judgements and manual annotation to help characterise the task and understand system performance . In the human preference evaluation , our best model outperforms template baselines and is preferred 40 % of the time to the gold standard Wikipedia reference . Code and data is available at https:// github.com / andychisholm / mimo .

Challenge: Wikipedia and other collaborative knowledge bases have coverage and quality issues especially on a long tail of specialist topics.
Approach: They propose a recurrent neural network sequence-to-sequence model with an attention mechanism trained on a multi-task autoencoding objective to generate one-sentence Wikipedia biographies from Wikidata.
Outcome: The proposed model achieves 41 BLEU score outperforming the baseline model and human annotators prefer the 40% of outputs as good as Wikipedia gold references.