[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-193080-105":53,"doc-detail-193080-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","2024-findings-acl-248","2024 Findings - ACL 248","","Results evaluate a text modeling approach across multiple settings using POS F1, Neg F1, and Macro F1. Comparisons include RoBERTa-base/large, ECPE variants, RankCP, KAG, Adapted, MuTEC, TSAM (base/large), KBCIN, and Window transformer, alongside the proposed POP-CEE method. Additional ablations remove prompt usage and components (intra, happiness, surprise, negative, basic), and test w/o constraint variants and constraint parameter sweeps, reporting how each factor impacts overall classification balance.",{"@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/2024-findings-acl-248/193080/",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/2024-findings-acl-248/193080.png","ImageObject",442,249,{"name":88,"@type":89},"Violet","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-03","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":79},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What evaluation metrics are used in the document?","Question",{"text":108,"@type":109},"The document reports Pos.F1(%), Neg.F1(%), and Macro F1(%). These metrics quantify performance for positive/negative cases and overall balance.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"Which method is compared against earlier baselines?",{"text":113,"@type":109},"POP-CEE is compared with RoBERTa-base/large and several task-specific baselines such as ECPE 2D, ECPE-MLL, RankCP, KAG, Adapted, MuTEC, TSAM, KBCIN, and Window transformer.",{"name":115,"@type":106,"acceptedAnswer":116},"What do the ablation tables analyze?",{"text":117,"@type":109},"They test the effect of removing the prompt and specific components like intra, happiness, surprise, negative, and basic, plus variants related to constraints, to show how each factor changes Macro F1 and class-specific F1.","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},193080,1789967182,{"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":79,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":15,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":79},4398048950312,"https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908","| posemo | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| happiness | 56.2% | 56.8% | 26.3% | 16.2% | 13.1% | 8.4% | 8.4% | 5.5% |\n| surprise | 42.2% | 84.6% | 5.2% | 7.1% | 1.8% | 1.7% | 0.6% | 0.8% |\n| sadness | 76.2% | 22.6% | 32.2% | 12.2% | 19.8% | 6.5% | 14.6% | 4.3% |\n| anger | 73.1% | 38.8% | 42.2% | 17.1% | 28.7% | 13.2% | 18.2% | 7.1% |\n| fear | 73.4% | 11.9% | 32.8% | 5.6% | 19.1% | 2.9% | 6.5% | 0.0% |\n| disgust | 83.9% | 27.1% | 31.2% | 7.4% | 24.3% | 3.4% | 6.8% | 2.9% |\n\n| Method | Pos.F1(%) | Neg.F1(%) | Macro F1(%) |\n| --- | --- | --- | --- |\n| RoBERTa-base(Poria et al., 2021) | 64.28 | 88.74 | 76.51 |\n| RoBERTa-large(Poria et al., 2021) | 66.23 | 87.89 | 77.06 |\n| ECPE 2D(Ding et al., 2020a) | 55.50 | 94.96 | 75.23 |\n| ECPE-MLL(Ding et al., 2020b) | 48.48 | 94.68 | 71.59 |\n| RankCP(Wei et al., 2020) | 33.00 | 97.30 | 65.15 |\n| KAG(Yan et al., 2021) | 55.52 | 94.49 | 75.02 |\n| Adapted(Turcan et al., 2021) | 62.47 | 95.67 | 79.07 |\n| MuTEC(Bhat and Modi, 2022) | 69.20 | 85.90 | 77.55 |\n| TSAM(base)(Zhang et al., 2022) | 68.50 | 89.75 | 79.17 |\n| TSAM(large)(Zhang et al., 2022) | 70.00 | 90.48 | 80.24 |\n| KBCIN(Zhao et al., 2022) | 68.59 | 89.65 | 79.12 |\n| Window transformer(Jiang et al., 2023) | 63.10 | 97.96 | 80.53 |\n| POP-CEE | 70.71* | 90.48 | 80.60 |\n\n\n| Method | Pos.F1(%) | Neg.F1(%) | Macro F1(%) |\n| --- | --- | --- | --- |\n| RoBERTa-base(Poria et al., 2021) | 28.02 | 95.67 | 61.85 |\n| RoBERTa-large(Poria et al., 2021) | 40.83 | 95.68 | 68.26 |\n| ECPE 2D(Ding et al., 2020a) | 28.67 | 97.39 | 63.03 |\n| ECPE-MLL(Ding et al., 2020b) | 20.23 | 93.55 | 57.65 |\n| RankCP(Wei et al., 2020) | 15.12 | 92.24 | 54.75 |\n| POP-CEE | 43.24* | 96.98 | 70.11 |\n\n\n| w/o prompt | Pos.F1(%) | Neg.F1(%) | MacroF1(%) |\n| --- | --- | --- | --- |\n| POP-CEE | 70.71 | 90.48 | 80.60 |\n| w/o intra | 68.37 | 89.84 | 79.11 |\n| w/o happiness | 70.27 | 90.24 | 80.25 |\n| w/o surprise | 67.41 | 89.88 | 78.64 |\n| w/o negative | 69.10 | 89.50 | 79.30 |\n| w/o basic | 69.14 | 90.24 | 79.69 |\n\n\n| w/o constraint | Pos.F1(%) | Neg.F1(%) | MacroF1(%) |\n| --- | --- | --- | --- |\n| POP-CEE | 70.71 | 90.48 | 80.60 |\n| w/o surprise | 68.18 | 89.91 | 79.04 |\n| w/o negative | 69.97 | 89.96 | 79.81 |\n| w/o basic | 68.16 | 90.03 | 79.09 |\n\n\n| constraint | l | Pos.F1(%) | Neg.F1(%) | MacroF1(%) |\n| --- | --- | --- | --- | --- |\n| basic | 1e1 | 68.45 | 90.16 | 79.30 |\n|  | 1e2 | 69.10 | 89.98 | 79.54 |\n|  | 1e3 | 68.80 | 90.15 | 79.47 |\n|  | 1e4 | 69.97 | 90.23 | 80.10 |\n|  | 1e5 | 70.71 | 90.48 | 80.60 |\n|  | 1e6 | 70.80 | 90.15 | 80.47 |\n|  | 1e7 | 70.70 | 89.84 | 80.27 |\n| surprise | 1e1 | 69.94 | 89.83 | 79.88 |\n|  | 1e2 | 70.64 | 90.16 | 80.40 |\n|  | 1e3 | 70.71 | 90.48 | 80.60 |\n|  | 1e4 | 69.46 | 89.83 | 79.65 |\n|  | 1e5 | 70.13 | 89.76 | 79.94 |\n|  | 1e6 | 68.71 | 89.68 | 79.20 |\n|  | 1e7 | 67.42 | 89.83 | 78.63 |\n| negative | 1e1 | 68.14 | 90.20 | 79.17 |\n|  | 1e2 | 69.11 | 90.24 | 79.68 |\n|  | 1e3 | 70.71 | 90.48 | 80.60 |\n|  | 1e4 | 69.95 | 90.06 | 80.01 |\n|  | 1e5 | 69.46 | 90.20 | 79.83 |\n|  | 1e6 | 70.75 | 90.19 | 80.47 |\n|  | 1e7 | 69.35 | 89.85 | 79.60 |\n\n\n| constraint | kbasic | Pos.F1(%) | Neg.F1(%) | MacroF1(%) |\n| --- | --- | --- | --- | --- |\n| basic | 0 | 66.15 | 90.01 | 78.08 |\n|  | 1 | 66.07 | 90.15 | 78.11 |\n|  | 2 | 69.08 | 90.20 | 79.64 |\n|  | 3 | 69.65 | 89.92 | 79.79 |\n|  | 4 | 70.40 | 89.77 | 80.09 |\n|  | 5 | 70.48 | 90.24 | 80.36 |\n|  | 6 | 70.71 | 90.48 | 80.60 |\n|  | 7 | 70.73 | 90.22 | 80.47 |\n|  | 8 | 70.77 | 89.92 | 80.34 |","cbCaihUJXx4O21HW","https://ap.wps.com/l/cbCaihUJXx4O21HW","pdf",1751633,"English","# Results overview\n## Main method comparisons\n## Ablation without prompt and components\n## Constraint analysis","[{\"question\":\"What evaluation metrics are used in the document?\",\"answer\":\"The document reports Pos.F1(%), Neg.F1(%), and Macro F1(%). These metrics quantify performance for positive/negative cases and overall balance.\"},{\"question\":\"Which method is compared against earlier baselines?\",\"answer\":\"POP-CEE is compared with RoBERTa-base/large and several task-specific baselines such as ECPE 2D, ECPE-MLL, RankCP, KAG, Adapted, MuTEC, TSAM, KBCIN, and Window transformer.\"},{\"question\":\"What do the ablation tables analyze?\",\"answer\":\"They test the effect of removing the prompt and specific components like intra, happiness, surprise, negative, and basic, plus variants related to constraints, to show how each factor changes Macro F1 and class-specific F1.\"}]","2024 Findings - ACL 248 | PDF",1788425798]