.

conversational_weather.json

@@ -4,9 +4,9 @@
       "has-leaderboard": "no",
       "leaderboard-url": "N/A",
       "leaderboard-description": "N/A",
-      "data-url": "https://github.com/facebookresearch/TreeNLG",
-      "paper-url": "https://aclanthology.org/P19-1080",
-      "paper-bibtext": "@inproceedings{balakrishnan-etal-2019-constrained,\n  title = \"Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue\",\n  author = \"Balakrishnan, Anusha  and\n    Rao, Jinfeng  and\n    Upasani, Kartikeya  and\n    White, Michael  and\n    Subba, Rajen\",\n  booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n  month = jul,\n  year = \"2019\",\n  address = \"Florence, Italy\",\n  publisher = \"Association for Computational Linguistics\",\n  url = \"https://www.aclweb.org/anthology/P19-1080\",\n  doi = \"10.18653/v1/P19-1080\",\n  pages = \"831--844\"\n}",
+      "data-url": "[Github](https://github.com/facebookresearch/TreeNLG)",
+      "paper-url": "[ACL Anthology](https://aclanthology.org/P19-1080)",
+      "paper-bibtext": "```\n@inproceedings{balakrishnan-etal-2019-constrained,\n  title = \"Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue\",\n  author = \"Balakrishnan, Anusha  and\n    Rao, Jinfeng  and\n    Upasani, Kartikeya  and\n    White, Michael  and\n    Subba, Rajen\",\n  booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n  month = jul,\n  year = \"2019\",\n  address = \"Florence, Italy\",\n  publisher = \"Association for Computational Linguistics\",\n  url = \"https://www.aclweb.org/anthology/P19-1080\",\n  doi = \"10.18653/v1/P19-1080\",\n  pages = \"831--844\"\n}\n```",
       "contact-name": "Kartikeya Upasani",
       "contact-email": "kart@fb.com"
     },
@@ -32,11 +32,14 @@
       "gem-added-by": "Vipul Raheja (Grammarly)"
     },
     "structure": {
-      "data-fields": "'gem_id': (string): GEM-formatted row id\n'id': (string): Row id in the original data\n'user_query': (string): Natural language weather query from humans\n'tree_str_mr': (string): Synthetically-added user context (datetime and location) in the form of a tree-structured MR\n\u2018response\u2019: (string): A tree-structured annotation of the response.\n",
-      "structure-example": "{'gem_id': 'weather-train-11',\n'id': '1108963',\n 'synthetic_user_context': '[__DG_INFORM__ [__ARG_TASK__ get_forecast ] '\n                           '[__ARG_TEMP__ 37 ] [__ARG_TEMP_UNIT__ fahrenheit ] '\n                           '[__ARG_CLOUD_COVERAGE__ partly cloudy ] '\n                           '[__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ currently ] '\n                           '] [__ARG_LOCATION__ [__ARG_CITY__ Oakland ] '\n                           '[__ARG_COUNTRY__ United States ] [__ARG_REGION__ '\n                           'California ] ] ] [__DG_INFORM__ [__ARG_TASK__ '\n                           'get_forecast ] [__ARG_TEMP_SUMMARY__ mid 40s ] '\n                           '[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '\n                           'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '\n                           'Oakland ] [__ARG_COUNTRY__ United States ] '\n                           '[__ARG_REGION__ California ] ] ] [__DG_INFORM__ '\n                           '[__ARG_TASK__ get_forecast ] '\n                           '[__ARG_CLOUD_COVERAGE__ mostly sunny ] '\n                           '[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '\n                           'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '\n                           'Oakland ] [__ARG_COUNTRY__ United States ] '\n                           '[__ARG_REGION__ California ] ] ]',\n 'tree_str_mr': \"[__DG_INFORM__ It's [__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ \"\n                'currently ] ] [__ARG_CLOUD_COVERAGE__ partly cloudy ] and '\n                '[__ARG_TEMP__ __ARG_TEMP__ ] [__ARG_TEMP_UNIT__ '\n                '__ARG_TEMP_UNIT__ ] [__ARG_LOCATION__ in [__ARG_CITY__ '\n                '__ARG_CITY__ ] , [__ARG_REGION__ __ARG_REGION__ ] , '\n                '[__ARG_COUNTRY__ __ARG_COUNTRY__ ] ] . ] [__DG_INFORM__ '\n                '[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This afternoon ] '\n                \"] , it'll be [__ARG_CLOUD_COVERAGE__ mostly sunny ] ] \"\n                '[__DG_INFORM__ with temperatures in the [__ARG_TEMP_SUMMARY__ '\n                'mid <number>  ] ]',\n 'user_query': 'Show weather forecast for Oakland, CA. '}",
-      "structure-splits": "Standard Splits: Train/Validation/Test\nAdditional Split: Disc_Test (a more challenging subset of the test set that contains discourse relations) ",
+      "data-fields": "- `gem_id`: (string): GEM-formatted row id\n- `id`: (string): Row id in the original data\n- `user_query`: (string): Natural language weather query from humans\n- `tree_str_mr`: (string): Synthetically-added user context (datetime and location) in the form of a tree-structured MR\n- `response`: (string): A tree-structured annotation of the response.\n",
+      "structure-example": "```\n{'gem_id': 'weather-train-11',\n'id': '1108963',\n 'synthetic_user_context': '[__DG_INFORM__ [__ARG_TASK__ get_forecast ] '\n                           '[__ARG_TEMP__ 37 ] [__ARG_TEMP_UNIT__ fahrenheit ] '\n                           '[__ARG_CLOUD_COVERAGE__ partly cloudy ] '\n                           '[__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ currently ] '\n                           '] [__ARG_LOCATION__ [__ARG_CITY__ Oakland ] '\n                           '[__ARG_COUNTRY__ United States ] [__ARG_REGION__ '\n                           'California ] ] ] [__DG_INFORM__ [__ARG_TASK__ '\n                           'get_forecast ] [__ARG_TEMP_SUMMARY__ mid 40s ] '\n                           '[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '\n                           'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '\n                           'Oakland ] [__ARG_COUNTRY__ United States ] '\n                           '[__ARG_REGION__ California ] ] ] [__DG_INFORM__ '\n                           '[__ARG_TASK__ get_forecast ] '\n                           '[__ARG_CLOUD_COVERAGE__ mostly sunny ] '\n                           '[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '\n                           'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '\n                           'Oakland ] [__ARG_COUNTRY__ United States ] '\n                           '[__ARG_REGION__ California ] ] ]',\n 'tree_str_mr': \"[__DG_INFORM__ It's [__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ \"\n                'currently ] ] [__ARG_CLOUD_COVERAGE__ partly cloudy ] and '\n                '[__ARG_TEMP__ __ARG_TEMP__ ] [__ARG_TEMP_UNIT__ '\n                '__ARG_TEMP_UNIT__ ] [__ARG_LOCATION__ in [__ARG_CITY__ '\n                '__ARG_CITY__ ] , [__ARG_REGION__ __ARG_REGION__ ] , '\n                '[__ARG_COUNTRY__ __ARG_COUNTRY__ ] ] . ] [__DG_INFORM__ '\n                '[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This afternoon ] '\n                \"] , it'll be [__ARG_CLOUD_COVERAGE__ mostly sunny ] ] \"\n                '[__DG_INFORM__ with temperatures in the [__ARG_TEMP_SUMMARY__ '\n                'mid <number>  ] ]',\n 'user_query': 'Show weather forecast for Oakland, CA. '}\n```",
+      "structure-splits": "- Standard Splits: Train/Validation/Test\n- Additional Split: Disc_Test (a more challenging subset of the test set that contains discourse relations) ",
       "structure-splits-criteria": "The test set contains 3,121 examples, of which 1.1K (35%) have unique MRs that have never been seen in the training set.",
-      "structure-outlier": "{'gem_id': 'weather-train-13333', 'data_id': '1260610', 'user_query': 'Sundown', 'tree_str_mr': '[__DG_INFORM__ [__ARG_TASK__ get_weather_attribute ] [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ 05:04 PM ] ] ]', 'response': '[__DG_INFORM__ The sun will go down at [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ __ARG_TIME__ ] ] ]'}"
+      "structure-outlier": "```\n{'gem_id': 'weather-train-13333', 'data_id': '1260610', 'user_query': 'Sundown', 'tree_str_mr': '[__DG_INFORM__ [__ARG_TASK__ get_weather_attribute ] [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ 05:04 PM ] ] ]', 'response': '[__DG_INFORM__ The sun will go down at [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ __ARG_TIME__ ] ] ]'}\n```"
+    },
+    "what": {
+      "dataset": "The purpose of this dataset is to assess how well a model can learn a template-like structure in a very low data setting. The task here is to produce a response to a weather-related query. The reply is further specified through the data attributes and discourse structure in the input. The output contains both the lexicalized text and discourse markers for attributes (e.g., `_ARG_TEMP_ 34`). "
     }
   },
   "curation": {