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These models rely on representation learning to select content appropriately, structure it coherently, and verbalize it grammatically, treating entities as nothing more than vocabulary tokens. In this work we propose an entity-centric neural architecture for data-to-text generation. Our model creates entity-specific representations which are dynamically updated. Text is generated conditioned on the data input and entity memory representations using hierarchical attention at each time step. We present experiments on the RotoWire benchmark and a (five times larger) new dataset on the baseball domain which we create. Our results show that the proposed model outperforms competitive baselines in automatic and human evaluation.","url_abs":"https://arxiv.org/abs/1906.03221v1","url_pdf":"https://arxiv.org/pdf/1906.03221v1.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":"data-to-text-generation-with-entity-modeling","repo_url":"https://github.com/ratishsp/data2text-entity-py","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"data-to-text-generation-with-entity-modeling","repo_url":"https://github.com/ratishsp/mlb-data-scripts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[{"slug":"mlb-dataset","name":"MLB Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/data-to-text-generation-on-mlb-dataset-2","task":"Data-to-Text Generation","dataset":"MLB Dataset","model":"ENT","rank_in_archive_order":3,"of":4,"metrics":{"BLEU":"11.50"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.03221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03221"}},"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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