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cs_restaurants.json

@@ -38,7 +38,7 @@
       "data-fields": "The data is stored in a JSON or CSV format, with identical contents. The data has 4 fields:\n* `da`: the input meaning representation/dialogue act (MR)\n* `delex_da`: the input MR, delexicalized -- all slot values are replaced with placeholders, such as `X-name`\n* `text`: the corresponding target natural language text (reference)\n* `delex_text`: the target text, delexicalized (delexicalization is applied regardless of inflection)\n\nIn addition, the data contains a JSON file with all possible inflected forms for all slot values in the dataset (`surface_forms.json`).\nEach slot -> value entry contains a list of inflected forms for the given value, with the base form (lemma), the inflected form, and\na [morphological tag](https://ufal.mff.cuni.cz/pdt/Morphology_and_Tagging/Doc/hmptagqr.html).\n\nThe same MR is often repeated multiple times with different synonymous reference texts.\n",
       "structure-splits": "| Property                       | Value |\n|--------------------------------|-------|\n| Total instances                | 5,192 |\n| Unique MRs                     | 2,417 |\n| Unique delexicalized instances | 2,752 |\n| Unique delexicalized MRs       |   248 |\n\nThe data is split in a roughly 3:1:1 proportion into training, development and test sections, making sure no delexicalized MR\nappears in two different parts. On the other hand, most DA types/intents are represented in all data parts.\n\n",
       "structure-description": "The data originated as a translation and localization of [Wen et al.'s SF restaurant](https://www.aclweb.org/anthology/D15-1199/) NLG dataset.\n",
-      "structure-example": "{\n  \"input\": \"inform_only_match(food=Turkish,name='\u0160vejk Restaurant',near='Charles Bridge',price_range=cheap)\",\n  \"target\": \"Na\u0161la jsem pouze jednu levnou restauraci pobl\u00ed\u017e Karlova mostu , kde pod\u00e1vaj\u00ed tureckou kuchyni , \u0160vejk Restaurant .\"\n}",
+      "structure-example": "```\n{\n  \"input\": \"inform_only_match(food=Turkish,name='\u0160vejk Restaurant',near='Charles Bridge',price_range=cheap)\",\n  \"target\": \"Na\u0161la jsem pouze jednu levnou restauraci pobl\u00ed\u017e Karlova mostu , kde pod\u00e1vaj\u00ed tureckou kuchyni , \u0160vejk Restaurant .\"\n}\n```",
       "structure-labels": "The input MRs were collected from [Wen et al.'s SF restaurant](https://www.aclweb.org/anthology/D15-1199/) NLG data\nand localized by randomly replacing slot values (using a list of Prague restaurant names, neighborhoods etc.).\n\nThe generated slot values were then automatically replaced in reference texts in the data.\n",
       "structure-splits-criteria": "The creators ensured that after delexicalization of the meaning representation there was no overlap between training and test. \n\nThe data is split at a 3:1:1 rate between training, validation, and test.",
       "structure-outlier": "n/a"