[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-194750-105":53,"doc-detail-194750-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","pengniro19","PengNiRo19","","This document presents a comparative analysis of different methods for Natural Language Generation (NLG), focusing on their accuracy and performance. It includes tables and data illustrating the results of various models such as Granroth-Wilding and Clark (2016), Wang et al. (2017), KnowSemLM, Seq2Seq, FES-LM, and EntityNLM. The tables showcase metrics like accuracy percentages, perplexity scores, and recall@30 values, allowing for a detailed evaluation of each method's effectiveness. Specific attention is given to KnowSemLM and its variations, highlighting improvements achieved through the incorporation of knowledge and fine-tuning. Perplexity scores are provided for FES-RNNLM and KnowSemLM, indicating their language modeling capabilities. Additionally, a section on Match/Event metrics for NYT and InScript datasets offers further insights into the models' performance in event detection and activation. The document serves as a technical reference for researchers and practitioners in the field of NLG, providing empirical data to support the development and selection of advanced language generation techniques.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/pengniro19/194750/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/pengniro19/194750.png","ImageObject",442,249,{"name":88,"@type":89},"Valentina","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-27","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the primary focus of this document?","Question",{"text":108,"@type":109},"The document focuses on comparing the accuracy and performance of various Natural Language Generation (NLG) methods.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"Which method showed the highest accuracy in the provided tables?",{"text":113,"@type":109},"Based on the tables, the 'Base w/ KnowSemLM' method achieved the highest accuracy at 76.15% among those listed.",{"name":115,"@type":106,"acceptedAnswer":116},"What metrics are used to evaluate the performance of the NLG methods?",{"text":117,"@type":109},"The document evaluates NLG methods using metrics such as accuracy percentages, perplexity, and Narrative Cloze Test (Recall@30).","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},194750,1790498085,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":47},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","| Method\u003Cbr>Granroth-Wilding and Clark (2016) Wang et al. (2017) | Accuracy\u003Cbr>49.57%\u003Cbr>55.12% |\n| --- | --- |\n| KnowSemLM w/o knowledge | 39.23% |\n| KnowSemLM w/o transit. & fine-tuning | 43.56% |\n| KnowSemLM w/o fine-tuning | 45.28% |\n| KnowSemLM | 56.27% |\n\n\n| Baselines | Accuracy |\n| --- | --- |\n| Seq2Seq | 58.0% |\n| Mostafazadeh et al. (2016) | 58.5% |\n| Seq2Seq with attention | 59.1% |\n| Model w/o Knowledge | S. M.V. |\n| FES-LM (Peng et al., 2017) | 62.3% 61.6% |\n| Knowledge Model | S. M.V. |\n| KnowSemLM | 66.5% 63. 1% |\n\n\n| Method | Accuracy |\n| --- | --- |\n| Base (Modi et al., 2017) | 62.65% |\n| EntityNLM (Ji et al., 2017) | 74.23% |\n| Base􀀃 | 60.58% |\n| Base􀀃 w/ FES-RNNLM | 63.79% |\n| Base􀀃 w/ KnowSemLM | 76.15% |\n\n\n| Perplexity |  |\n| --- | --- |\n| FES-RNNLM | 121.8 |\n| KnowSemLM w/o transitivity | 120.7 |\n| KnowSemLM | 120.4 |\n| Narrative Cloze Test (Recall@30) |  |\n| FES-RNNLM | 47.9 |\n| KnowSemLM w/o transitivity | 49.3 |\n| KnowSemLM | 49.6 |\n\n\n| Match/Event |  | Activation/Event | 􀀕 |\n| --- | --- | --- | --- |\n| NYT | 0.13 | 0.03 | 0.36 |\n| InScript | 0.82 | 0.28 | 0.46 |","cbCainXdqZJYOsiu","https://ap.wps.com/l/cbCainXdqZJYOsiu","pdf",397911,13,"English","# Accuracy Comparison\n## Granroth-Wilding and Clark (2016) vs. Wang et al. (2017)\n## KnowSemLM Performance\n## Baselines Comparison\n## Perplexity and Narrative Cloze Test Results\n## Match/Event Metrics","[{\"question\":\"What is the primary focus of this document?\",\"answer\":\"The document focuses on comparing the accuracy and performance of various Natural Language Generation (NLG) methods.\"},{\"question\":\"Which method showed the highest accuracy in the provided tables?\",\"answer\":\"Based on the tables, the 'Base w/ KnowSemLM' method achieved the highest accuracy at 76.15% among those listed.\"},{\"question\":\"What metrics are used to evaluate the performance of the NLG methods?\",\"answer\":\"The document evaluates NLG methods using metrics such as accuracy percentages, perplexity, and Narrative Cloze Test (Recall@30).\"}]","PengNiRo19 | PDF",1788442241]