.

dart.json

@@ -2,14 +2,14 @@
   "overview": {
     "where": {
       "has-leaderboard": "yes",
-      "leaderboard-url": "https://github.com/Yale-LILY/dart#leaderboard",
+      "leaderboard-url": "[Leaderboard](https://github.com/Yale-LILY/dart#leaderboard)",
       "leaderboard-description": "Several state-of-the-art table-to-text models were evaluated on DART, such as BART ([Lewis et al., 2020](https://arxiv.org/pdf/1910.13461.pdf)), Seq2Seq-Att ([MELBOURNE](https://webnlg-challenge.loria.fr/files/melbourne_report.pdf)) and End-to-End Transformer ([Castro Ferreira et al., 2019](https://arxiv.org/pdf/1908.09022.pdf)).\nThe leaderboard reports BLEU, METEOR, TER, MoverScore, BERTScore and BLEURT scores.",
       "website": "n/a",
-      "data-url": "https://github.com/Yale-LILY/dart",
-      "paper-url": "https://aclanthology.org/2021.naacl-main.37/",
+      "data-url": "[Github](https://github.com/Yale-LILY/dart)",
+      "paper-url": "[ACL Anthology](https://aclanthology.org/2021.naacl-main.37/)",
       "contact-email": "{dragomir.radev, r.zhang}@yale.edu, {nazneen.rajani}@salesforce.com",
       "contact-name": "Dragomir Radev, Rui Zhang, Nazneen Rajani",
-      "paper-bibtext": "@inproceedings{nan-etal-2021-dart,\n    title = \"{DART}: Open-Domain Structured Data Record to Text Generation\",\n    author = \"Nan, Linyong  and\n      Radev, Dragomir  and\n      Zhang, Rui  and\n      Rau, Amrit  and\n      Sivaprasad, Abhinand  and\n      Hsieh, Chiachun  and\n      Tang, Xiangru  and\n      Vyas, Aadit  and\n      Verma, Neha  and\n      Krishna, Pranav  and\n      Liu, Yangxiaokang  and\n      Irwanto, Nadia  and\n      Pan, Jessica  and\n      Rahman, Faiaz  and\n      Zaidi, Ahmad  and\n      Mutuma, Mutethia  and\n      Tarabar, Yasin  and\n      Gupta, Ankit  and\n      Yu, Tao  and\n      Tan, Yi Chern  and\n      Lin, Xi Victoria  and\n      Xiong, Caiming  and\n      Socher, Richard  and\n      Rajani, Nazneen Fatema\",\n    booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n    month = jun,\n    year = \"2021\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.naacl-main.37\",\n    doi = \"10.18653/v1/2021.naacl-main.37\",\n    pages = \"432--447\",\n    abstract = \"We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.\",\n}"
+      "paper-bibtext": "```\n@inproceedings{nan-etal-2021-dart,\n    title = \"{DART}: Open-Domain Structured Data Record to Text Generation\",\n    author = \"Nan, Linyong  and\n      Radev, Dragomir  and\n      Zhang, Rui  and\n      Rau, Amrit  and\n      Sivaprasad, Abhinand  and\n      Hsieh, Chiachun  and\n      Tang, Xiangru  and\n      Vyas, Aadit  and\n      Verma, Neha  and\n      Krishna, Pranav  and\n      Liu, Yangxiaokang  and\n      Irwanto, Nadia  and\n      Pan, Jessica  and\n      Rahman, Faiaz  and\n      Zaidi, Ahmad  and\n      Mutuma, Mutethia  and\n      Tarabar, Yasin  and\n      Gupta, Ankit  and\n      Yu, Tao  and\n      Tan, Yi Chern  and\n      Lin, Xi Victoria  and\n      Xiong, Caiming  and\n      Socher, Richard  and\n      Rajani, Nazneen Fatema\",\n    booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n    month = jun,\n    year = \"2021\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.naacl-main.37\",\n    doi = \"10.18653/v1/2021.naacl-main.37\",\n    pages = \"432--447\",\n    abstract = \"We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.\",\n}\n```"
     },
     "languages": {
       "is-multilingual": "no",
@@ -37,12 +37,15 @@
     },
     "structure": {
       "data-fields": "-`tripleset`: a list of tuples, each tuple has 3 items\n-`subtree_was_extended`: a boolean variable (true or false)\n-`annotations`: a list of dict, each with source and text keys.\n-`source`: a string mentioning the name of the source table.\n-`text`: a sentence string.\n",
-      "structure-example": " {\n    \"tripleset\": [\n      [\n        \"Ben Mauk\",\n        \"High school\",\n        \"Kenton\"\n      ],\n      [\n        \"Ben Mauk\",\n        \"College\",\n        \"Wake Forest Cincinnati\"\n      ]\n    ],\n    \"subtree_was_extended\": false,\n    \"annotations\": [\n      {\n        \"source\": \"WikiTableQuestions_lily\",\n        \"text\": \"Ben Mauk, who attended Kenton High School, attended Wake Forest Cincinnati for college.\"\n      }\n    ]\n  }",
+      "structure-example": "```\n {\n    \"tripleset\": [\n      [\n        \"Ben Mauk\",\n        \"High school\",\n        \"Kenton\"\n      ],\n      [\n        \"Ben Mauk\",\n        \"College\",\n        \"Wake Forest Cincinnati\"\n      ]\n    ],\n    \"subtree_was_extended\": false,\n    \"annotations\": [\n      {\n        \"source\": \"WikiTableQuestions_lily\",\n        \"text\": \"Ben Mauk, who attended Kenton High School, attended Wake Forest Cincinnati for college.\"\n      }\n    ]\n  }\n```",
       "structure-splits": "|Input Unit | Examples | Vocab Size | Words per SR | Sents per SR | Tables |\n| ------------- | ------------- || ------------- || ------------- || ------------- || ------------- |\n|Triple Set | 82,191 | 33.2K | 21.6 | 1.5 | 5,623 |\n\n| Train | Dev | Test|\n| ------------- | ------------- || ------------- |\n| 62,659 | 6,980 | 12,552|\n\n\nStatistics of DART decomposed by different collection methods. DART exhibits a great deal of topical variety in terms of the number of unique predicates, the number of unique triples, and the vocabulary size. These statistics are computed from DART v1.1.1; the number of unique predicates reported is post-unification (see Section 3.4). SR: Surface Realization.\n([details in Table 1 and 2](https://arxiv.org/pdf/2007.02871.pdf)).\n",
       "structure-description": "The structure is supposed to be able more complex structures beyond \"flat\" attribute-value pairs, instead encoding hierarchical relationships.",
       "structure-labels": "They are a combination of those from existing datasets and new annotations that take advantage of the hierarchical structure",
       "structure-splits-criteria": "For WebNLG 2017 and Cleaned E2E,  DART use the original data splits. For the new annotation on WikiTableQuestions and WikiSQL, random splitting will make train, dev, and test splits contain similar tables and similar <triple-set, sentence> examples. They are thus split based on Jaccard similarity such that no training examples has a similarity with a test example of over 0.5",
       "structure-outlier": "n/a"
+    },
+    "what": {
+      "dataset": "DART is an English dataset aggregating multiple other data-to-text dataset in a common triple-based format. The new format is completely flat, thus not requiring a model to learn hierarchical structures, while still retaining the full information. "
     }
   },
   "curation": {