{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/operations-guided-neural-networks-for-high","title":"Operations Guided Neural Networks for High Fidelity Data-To-Text Generation","arxiv_id":"1809.02735","date":"2018-09-08","proceeding":null,"authors":["Feng Nie","Jinpeng Wang","Jin-Ge Yao","Rong pan","Chin-Yew Lin"],"abstract":"Recent neural models for data-to-text generation are mostly based on\ndata-driven end-to-end training over encoder-decoder networks. Even though the\ngenerated texts are mostly fluent and informative, they often generate\ndescriptions that are not consistent with the input structured data. This is a\ncritical issue especially in domains that require inference or calculations\nover raw data. In this paper, we attempt to improve the fidelity of neural\ndata-to-text generation by utilizing pre-executed symbolic operations. We\npropose a framework called Operation-guided Attention-based\nsequence-to-sequence network (OpAtt), with a specifically designed gating\nmechanism as well as a quantization module for operation results to utilize\ninformation from pre-executed operations. Experiments on two sports datasets\nshow our proposed method clearly improves the fidelity of the generated texts\nto the input structured data.","url_abs":"http://arxiv.org/abs/1809.02735v1","url_pdf":"http://arxiv.org/pdf/1809.02735v1.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":"operations-guided-neural-networks-for-high","repo_url":"https://github.com/janenie/espn-nba-data","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.02735","atlas_url":"https://app.syntology.ai/?focus=1809.02735","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}