.

totto.json

@@ -6,8 +6,8 @@
       "leaderboard-description": "This dataset has an associated, active [leaderboard](https://github.com/google-research-datasets/totto#leaderboard) maintained by the authors.\nThe test set ground truth targets / references are private, i.e they are not publicly shared or downloadable - hence, leaderboard submission is necessary for test set evaluation.\nTo evaluate your model on the dev or test set AND/OR submit to the leaderboard, you need to submit your model files through this [form](https://forms.gle/AcF9TRqWrPhPzztt7) (The form provides an option to opt-out of going on the leaderboard).\n\nThe leaderboard reports three sets of BLEU, PARENT and BLEURT scores for each submission - on the overall test set, the *Overlap* subset of the test set and the *non-Overlap* subset of the test set.\n",
       "data-url": "[ToTTo Main Repo](https://github.com/google-research-datasets/totto) + [ToTTo Supplementary Repo](https://github.com/google-research/language/tree/master/language/totto)",
       "website": "n/a",
-      "paper-bibtext": "@inproceedings{parikh-etal-2020-totto,\n    title = \"{ToTTo}: A Controlled Table-To-Text Generation Dataset\",\n    author = \"Parikh, Ankur  and\n      Wang, Xuezhi  and\n      Gehrmann, Sebastian  and\n      Faruqui, Manaal  and\n      Dhingra, Bhuwan  and\n      Yang, Diyi  and\n      Das, Dipanjan\",\n    booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n    month = nov,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2020.emnlp-main.89\",\n    doi = \"10.18653/v1/2020.emnlp-main.89\",\n    pages = \"1173--1186\",\n    abstract = \"We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.\",\n}",
-      "paper-url": "https://aclanthology.org/2020.emnlp-main.89",
+      "paper-bibtext": "```\n@inproceedings{parikh-etal-2020-totto,\n    title = \"{ToTTo}: A Controlled Table-To-Text Generation Dataset\",\n    author = \"Parikh, Ankur  and\n      Wang, Xuezhi  and\n      Gehrmann, Sebastian  and\n      Faruqui, Manaal  and\n      Dhingra, Bhuwan  and\n      Yang, Diyi  and\n      Das, Dipanjan\",\n    booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n    month = nov,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2020.emnlp-main.89\",\n    doi = \"10.18653/v1/2020.emnlp-main.89\",\n    pages = \"1173--1186\",\n    abstract = \"We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.\",\n}\n```",
+      "paper-url": "[ACL Anthology](https://aclanthology.org/2020.emnlp-main.89)",
       "contact-email": "totto@google.com",
       "contact-name": "Ankur Parikh"
     },
@@ -42,6 +42,9 @@
       "structure-example": "The main repository's `README.md` already provides a thorough walkthrough of data instances and fields [here](https://github.com/google-research-datasets/totto#dataset-description)\n\nBelow is the instance for a table from the wiki-page for the musical artist _Weird Al' Yankovic_ , likely listing his on-television appearances.\n\n```\n    {\n      \"table_page_title\": \"'Weird Al' Yankovic\",\n      \"table_webpage_url\": \"https://en.wikipedia.org/wiki/%22Weird_Al%22_Yankovic\",\n      \"table_section_title\": \"Television\",\n      \"table_section_text\": \"\",\n      \"table\": \"[Described below]\",\n      \"highlighted_cells\": [[22, 2], [22, 3], [22, 0], [22, 1], [23, 3], [23, 1], [23, 0]],\n      \"example_id\": 12345678912345678912,\n      \"sentence_annotations\": [{\"original_sentence\": \"In 2016, Al appeared in 2 episodes of BoJack Horseman as Mr. Peanutbutter's brother, Captain Peanutbutter, and was hired to voice the lead role in the 2016 Disney XD series Milo Murphy's Law.\",\n\t\t      \"sentence_after_deletion\": \"In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter, and was hired to the lead role in the 2016 series Milo Murphy's Law.\",\n\t\t      \"sentence_after_ambiguity\": \"In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter, and was hired for the lead role in the 2016 series Milo Murphy's 'Law.\",\n\t\t      \"final_sentence\": \"In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter and was hired for the lead role in the 2016 series Milo Murphy's Law.\"}],\n    }\n```\n\nThe `table` field is expanded as below:\n\n```\n    [\n     [\n        {\n            \"column_span\": 1,\n             \"is_header\": true,\n             \"row_span\": 1,\n             \"value\": \"Year\"},\n        {    \"column_span\": 1,\n             \"is_header\": true,\n             \"row_span\": 1,\n             \"value\": \"Title\"},\n        {    \"column_span\": 1,\n             \"is_header\": true,\n             \"row_span\": 1,\n             \"value\": \"Role\"},\n        {    \"column_span\": 1,\n             \"is_header\": true,\n             \"row_span\": 1,\n             \"value\": \"Notes\"}\n      ],\n      [\n        {    \"column_span\": 1,\n             \"is_header\": false,\n             \"row_span\": 1,\n             \"value\": \"1997\"},\n        {    \"column_span\": 1,\n             \"is_header\": false,\n             \"row_span\": 1,\n             \"value\": \"Eek! The Cat\"},\n        {    \"column_span\": 1,\n             \"is_header\": false,\n             \"row_span\": 1,\n             \"value\": \"Himself\"},\n        {    \"column_span\": 1,\n             \"is_header\": false,\n             \"row_span\": 1,\n             \"value\": \"Episode: 'The FugEektive'\"}\n      ], ...\n    ]\n```\n\nThe [Supplementary Repo](https://github.com/google-research/language/tree/master/language/totto) also provides browsable samples under its `sample/` folder. It additionally provides HTML visualization scripts with their outputs located under the aforementioned folder. The instructions to access and visualize these samples can also be found [here](https://github.com/google-research/language/tree/master/language/totto#visualizing-sample-data).\n",
       "structure-splits-criteria": "The dev and test splits are further equally distributed between  _Overlap_ and _non-Overlap_ .\nThe examples in the _Overlap_ set are harder on account of the domain shift resulting from them having none of their header (row and column) names in common with those seen during training.\n\nRefer to Table 5 in the paper for a more extensive list of properties about table size, target vocabulary etc and their aggregates.\n",
       "structure-outlier": "There are some very large tables in the dataset with thousands of rows. Table 7 shows some of the challenges of the dataset, showing that very few examples require access to the table description itself which makes those examples an outlier. "
+    },
+    "what": {
+      "dataset": "ToTTo is a high-quality English table-to-text dataset with more than 100,000 examples in which a table from Wikipedia with highlighted cells is paired with a sentence that describes the highlighted cells. All examples in the dataset were post-edited in multiple steps to ensure that the targets are fully faithful to the input information."
     }
   },
   "curation": {