[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-195217-105":53,"doc-detail-195217-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","d19-1018","D19-1018","","Evaluation snippets compare review, tip, and justification text quality using datasets (Yelp, Amazon Clothing) and aspect-based generation outputs. Multiple neural models and variants are tested, reporting metrics such as BLEU-3, BLEU-4, Distinct-1, and Distinct-2, alongside R/I/D scores for different reference strategies (review, tip, top-k). The content includes example generated sentences, ground-truth references, and masked-language prompts to illustrate how model persona and aspect coverage affect final text.",{"@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":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/d19-1018/195217/",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/d19-1018/195217.png","ImageObject",442,249,{"name":88,"@type":89},"Clementine","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-10","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 evaluation metrics are reported in the document?","Question",{"text":108,"@type":109},"The document reports BLEU-3 and BLEU-4, Distinct-1 and Distinct-2, and R/I/D scores to compare different models and strategies.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"Which datasets are used for the experiments?",{"text":113,"@type":109},"The experiments use Yelp and Amazon Clothing datasets, each with train/dev/test splits and aspect counts.",{"name":115,"@type":106,"acceptedAnswer":116},"How do reference strategies differ in the results?",{"text":117,"@type":109},"Results compare using different reference types such as Review, Tip, and the general Ref2Seq setup, including a Top-k variant to influence generation diversity.","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},195217,1788446448,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"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":125,"read_time":79},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","| Review examples: |\n| --- |\n| I love this little stand! The coconut mocha chiller and caramel macchiato are delicious.\u003Cbr>Wow what a special find. One of the most unique and special date nights my husband and I have had. |\n| Tip examples: |\n| Great food. Nice ambiance. Gnocchi were very good.\u003Cbr>I can't get enough of this place. |\n| Justification examples: |\n| The food portions were huge.\u003Cbr>Plain cheese quesadilla is very good and very cheap. |\n\n\n| BOW-Xgboost | 0.559 | 0.679 | 0.475 |\n| --- | --- | --- | --- |\n| CNN | 0.644 | 0.596 | 0.700 |\n| LSTM-MaxPool | 0.675 | 0.703 | 0.650 |\n| BERT | 0.747 | 0.700 | 0.800 |\n| BERT-SA (one epoch) | 0.481 | 0.975 | 0.320 |\n| BERT-SA (three epoch) | 0.491 | 1.000 | 0.325 |\n\n\n| The Tuna is pretty amazing\u003Cbr>Appetizers and pasta are excellent here\u003Cbr>An excellent selection of both sweet and savory crepes It was filled with delicious food, fantastic music and dancing |\n| --- |\n| Amazon-Cloth |\n| The quality of the material is great\u003Cbr>Great shirt, especially for the price. The seams and stitching are really nice Fit the bill for a Halloween costume. |\n\n| Iter 0 | universe [MASK] is extremely friendly and persona \\#\\#ble |\n| --- | --- |\n| Iter 5 | the [MASK] is extremely friendly and persona \\#\\#ble |\n| Iter 10 | the [MASK] is extremely friendly and persona \\#\\#ble |\n| Iter 15 | the staff are extremely cool and persona \\#\\#ble |\n| Iter 20 | the staff are extra kind , persona \\#\\#ble |\n\n\n| Dataset | Train | Dev | Test | \\# Users | \\# Items | \\# Aspects |\n| --- | --- | --- | --- | --- | --- | --- |\n| Yelp | 1,219,962 | 115,907 | 115,907 | 115,907 | 51,948 | 2,041 |\n| Amazon Clothing | 202,528 | 57,947 | 57,947 | 57,947 | 50,240 | 581 |\n\n\n| Model | BLEU-3 | BLEU-4 | Distinct-1 | Distinct-2  BLEU-3 |  | BLEU-4 | Distinct-1 | Distinct-2 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Item-Rand | 0.440 | 0.150 | 2.766 | 20.151 | 1.620 | 0.680 | 2.400 | 11.853 |\n| LexRank | 2.290 | 0.920 | 1.738 | 8.509 | 3.480 | 2.250 | 2.407 | 14.956 |\n| Attr2seq | 7.890 | 0.000 | 0.049 | 0.095 | 1.720 | 0.560 | 0.076 | 0.352 |\n| Ref2Seq | 4.380 | 2.450 | 0.188 | 1.163 | 8.780 | 5.670 | 0.141 | 1.240 |\n| AP-Ref2Seq | 3.390 | 1.830 | 0.326 | 2.094 | 13.910 | 12.500 | 0.557 | 3.661 |\n| Ref2Seq (Top-k) | 1.630 | 0.700 | 0.818 | 11.927 | 3.960 | 2.130 | 0.697 | 10.858 |\n| ACMLM | 0.700 | 0.280 | 1.322 | 14.319 2.420 |  | 1.590 | 0.942 | 9.312 |\n\n| Model | R | I | D |\n| --- | --- | --- | --- |\n| Ref2Seq (Review) | 3.02 | 2.39 | 2.10 |\n| Ref2Seq (Tip) | 3.25 | 2.35 | 2.34 |\n| Ref2Seq | 3.87 | 3.13 | 2.96 |\n| Ref2Seq (Top-k) | 3.95 | 3.34 | 3.39 |\n| ACMLM | 3.23 | 3.29 | 3.42 |\n\n\n| Model | Shake Shack | Teharu Sushi | MGM Grand Hotel |\n| --- | --- | --- | --- |\n| Ground Truth | The burger was good | The rolls are pretty great , typical rolls not that many specials | Room was very clean comfortable |\n| LexRank | A great burger and fries. | Sushi ? | Great rooms. |\n| Ref2Seq (Review) | i love trader joe 's , i love trader joe 's | the food was good and the service was great | i love this place ! the food is always good and the service is always great |\n| Ref2Seq (Tip) | this place is awesome | love this place | come here |\n| Ref2Seq | this place has some of the best burgers | the sushi is delicious | the room was nice |\n| Ref2Seq (Top-k) | the fries are amazing | fresh and delicious sushi | open hotel for hours |\n| ACMLM | breakfast sandwiches are overall very filling | overall fun experience with half price sushi | family style dinner , long time shopping trip to vegas, family dining , cheap lunch |\n\n\n| Dataset | Aspects | Generated Output |\n| --- | --- | --- |\n| Yelp | dining pastry chicken\u003Cbr>sandwich | the dining room is nice the pastries were pretty good the chicken fried rice is the best the pulled pork sandwich is the best thing on the menu |\n| Amazon\u003Cbr>Clothing | product | great product , fast shippong |\n|  | price | design is nice , good price |\n|  | leather | comfortable leather sneakers . classic |\n| ","cbCaium1rxJIhLSV","https://ap.wps.com/l/cbCaium1rxJIhLSV","pdf",545946,10,"English","# Model Evaluation\n## Datasets\n## Generated Outputs and Ground Truth\n## Metrics (BLEU, Distinct, R/I/D)\n## Example Prompts and Iterations","[{\"question\":\"What evaluation metrics are reported in the document?\",\"answer\":\"The document reports BLEU-3 and BLEU-4, Distinct-1 and Distinct-2, and R/I/D scores to compare different models and strategies.\"},{\"question\":\"Which datasets are used for the experiments?\",\"answer\":\"The experiments use Yelp and Amazon Clothing datasets, each with train/dev/test splits and aspect counts.\"},{\"question\":\"How do reference strategies differ in the results?\",\"answer\":\"Results compare using different reference types such as Review, Tip, and the general Ref2Seq setup, including a Top-k variant to influence generation diversity.\"}]","D19-1018 | PDF"]