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We build our generation framework based on a\npointer network which can copy facts from the input KB, and add two attention\nmechanisms: (i) slot-aware attention to capture the association between a slot\ntype and its corresponding slot value; and (ii) a new \\emph{table position\nself-attention} to capture the inter-dependencies among related slots. For\nevaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we\npropose a KB reconstruction based metric by extracting a KB from the generation\noutput and comparing it with the input KB. We also create a new data set which\nincludes 106,216 pairs of structured KBs and their corresponding natural\nlanguage descriptions for two distinct entity types. Experiments show that our\napproach significantly outperforms state-of-the-art methods. The reconstructed\nKB achieves 68.8% - 72.6% F-score.","url_abs":"http://arxiv.org/abs/1809.01797v2","url_pdf":"http://arxiv.org/pdf/1809.01797v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"describing-a-knowledge-base","repo_url":"https://github.com/EagleW/Describing_a_Knowledge_Base","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"task_slug":"kb-to-language-generation","task_name":"KB-to-Language Generation"},{"task_slug":"table-to-text-generation","task_name":"Table-to-Text Generation"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[{"slug":"wikipedia-person-and-animal-dataset","name":"Wikipedia Person and Animal Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/kb-to-language-generation-on-wikipedia-person","task":"KB-to-Language Generation","dataset":"Wikipedia Person and Animal Dataset","model":"KB-to-Language Generation Model","rank_in_archive_order":1,"of":1,"metrics":{"BLEU":"23.2","METEOR":"23.4","ROUGE":"42.0"},"uses_additional_data":false},{"leaderboard":"/sota/table-to-text-generation-on-wikipedia-person","task":"Table-to-Text Generation","dataset":"Wikipedia Person and Animal Dataset","model":"KB-to-Language Generation Model","rank_in_archive_order":2,"of":2,"metrics":{"BLEU":"23.2","METEOR":"42.0","ROUGE":"23.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.01797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.01797"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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