{"url":"/sota/table-to-text-generation-on-wikibio","task":{"name":"Table-to-Text Generation","url":"/task/table-to-text-generation","note":null},"dataset":{"name":"WikiBio","url":"/dataset/wikibio"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**Here is the provided data converted into a table format for clarity:\r\nCOUNTRIES\t1971-2010\t2011\t2012\t2013\t2014\t2015\t2016\t2017\t2018\r\n\t\t\t\t\t\t\t\t\t\r\nSaudi Arabia\t2742962\t222247\t358560\t270502\t312489\t522750\t462598\t143363\t100910\r\nU.A.E\t1595574\t156353\t182630\t273234\t350522\t326986\t295647\t275436\t208635\r\nOman\t394436\t53525\t69407\t47794\t39793\t47788\t45085\t42362\t27202\r\nQatar\t82043\t5121\t7320\t8119\t10042\t12741\t9706\t11592\t20993\r\nBahrain\t94599\t10641\t10530\t9600\t9226\t9029\t8226\t7919\t5745\r\nKuwait\t180755\t173\t5\t229\t132\t164\t770\t773\t493\r\nSouth Korea\t15343\t12\t7\t12\t46\t13\t17\t9\t13\r\nMalaysia\t23410\t2092\t1309\t2031\t20577\t20216\t10625\t7174\t9881\r\nChina\t1717\t180\t220\t155\t254\t355\t482\t457\t854\r\nAlgeria\t878\t7\t2\t7\t36\t211\t259\t461\t213\r\nAngola\t601\t8\t6\t8\t1\t22\t22\t12\t11\r\nAzerbaijan \t51\t0\t3\t98\t22\t8\t8\t8\t20\r\nBrunei\t998\t79\t74\t67\t48\t85\t85\t212\t225\r\nCameroon\t48\t15\t0\t0\t3\t2\t0\t1\t4\r\nCroatia\t44\t1\t0\t0\t0\t0\t0\t0\t0\r\nCyprus\t922\t71\t129\t111\t278\t500\t990\t1729\t1644\r\nGabon\t299\t2\t4\t1\t8\t0\t0\t2\t0\r\nGen-Island\t195\t0\t0\t0\t0\t2\t0\t0\t0\r\nGermany\t187\t11\t23\t26\t23\t43\t38\t64\t103\r\nGreece\t542\t0\t0\t0\t0\t2\t3\t2\t3\r\nGuinea\t144\t15\t12\t13\t6\t10\t11\t6\t11\r\nHong Kong\t252\t26\t17\t20\t38\t29\t38\t54\t57\r\nIran\t12586\t14\t3\t26\t5\t65\t37\t100\t20\r\nIraq\t68135\t0\t32\t951\t1041\t709\t543\t599\t756\r\nItaly\t17763\t2875\t3361\t2068\t1563\t431\t242\t141\t86\r\nJapan\t380\t48\t62\t44\t69\t82\t102\t153\t258\r\nJordan\t5341\t178\t279\t345\t328\t321\t282\t285\t170\r\nKenya\t67\t11\t8\t6\t3\t11\t15\t8\t17\r\nLebanon\t432\t30\t23\t15\t57\t33\t42\t24\t27\r\nLibya\t72112\t490\t1872\t4543\t2121\t8\t0\t4\t8\r\nMorocco\t44\t0\t0\t0\t2\t0\t0\t1\t5\r\nNigeria\t2665\t166\t142\t117\t113\t106\t104\t75\t115","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["BLEU","ROUGE","PARENT"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"BLEU":"higher","ROUGE":"higher","PARENT":null}},"counts":{"rows":4,"rows_with_code":4,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Field-gating Seq2seq + dual attention","metrics":{"BLEU":"44.89","ROUGE":"41.21"},"uses_additional_data":false,"paper_date":"2017-11-27","paper":"/paper/table-to-text-generation-by-structure-aware","paper_url":"http://arxiv.org/abs/1711.09724v1","paper_title":"Table-to-text Generation by Structure-aware Seq2seq Learning","code":"https://github.com/tyliupku/wiki2bio","n_code_links":3,"syntology":null},{"rank_in_archive_order":2,"model":"Field-gating Seq2seq + dual attention + beam search","metrics":{"BLEU":"44.71","ROUGE":"41.65"},"uses_additional_data":false,"paper_date":"2017-11-27","paper":"/paper/table-to-text-generation-by-structure-aware","paper_url":"http://arxiv.org/abs/1711.09724v1","paper_title":"Table-to-text Generation by Structure-aware Seq2seq Learning","code":"https://github.com/tyliupku/wiki2bio","n_code_links":3,"syntology":null},{"rank_in_archive_order":3,"model":"MBD","metrics":{"BLEU":"41.56","PARENT":"56.16"},"uses_additional_data":false,"paper_date":"2021-02-04","paper":"/paper/controlling-hallucinations-at-word-level-in","paper_url":"https://arxiv.org/abs/2102.02810v2","paper_title":"Controlling Hallucinations at Word Level in Data-to-Text Generation","code":"https://github.com/KaijuML/dtt-multi-branch","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"Table NLM","metrics":{"BLEU":"34.70","ROUGE":"25.80"},"uses_additional_data":false,"paper_date":"2016-03-24","paper":"/paper/neural-text-generation-from-structured-data","paper_url":"http://arxiv.org/abs/1603.07771v3","paper_title":"Neural Text Generation from Structured Data with Application to the Biography Domain","code":"https://github.com/parajain/data-to-text","n_code_links":2,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}