.

ART.json

@@ -5,9 +5,9 @@
       "leaderboard-url": "N/A",
       "leaderboard-description": "N/A",
       "website": "http://abductivecommonsense.xyz/",
-      "data-url": "https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip",
-      "paper-url": "https://openreview.net/pdf?id=Byg1v1HKDB",
-      "paper-bibtext": "@inproceedings{\nBhagavatula2020Abductive,\ntitle={Abductive Commonsense Reasoning},\nauthor={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi},\nbooktitle={International Conference on Learning Representations},\nyear={2020},\nurl={https://openreview.net/forum?id=Byg1v1HKDB}\n}",
+      "data-url": "[Link](https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip)",
+      "paper-url": "[Link](https://openreview.net/pdf?id=Byg1v1HKDB)",
+      "paper-bibtext": "```\n@inproceedings{\nBhagavatula2020Abductive,\ntitle={Abductive Commonsense Reasoning},\nauthor={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi},\nbooktitle={International Conference on Learning Representations},\nyear={2020},\nurl={https://openreview.net/forum?id=Byg1v1HKDB}\n}\n```",
       "contact-name": "Chandra Bhagavatulla",
       "contact-email": "chandrab@allenai.org"
     },
@@ -33,10 +33,13 @@
       "gem-added-by": "Chandra Bhagavatula (AI2), Ronan LeBras (AI2), Aman Madaan (CMU), Nico Daheim (RWTH Aachen University)"
     },
     "structure": {
-      "data-fields": "- observation_1: A string describing an observation / event.\n- observation_2: A string describing an observation / event.\n- label: A string that plausibly explains why observation_1 and observation_2 might have happened.",
+      "data-fields": "- `observation_1`: A string describing an observation / event.\n- `observation_2`: A string describing an observation / event.\n- `label`: A string that plausibly explains why observation_1 and observation_2 might have happened.",
       "structure-labels": "Explanations were authored by crowdworkers on the Amazon Mechanical Turk platform using a custom template designed by the creators of the dataset.",
-      "structure-example": "{\n'gem_id': 'GEM-ART-validation-0',\n'observation_1': 'Stephen was at a party.',\n'observation_2': 'He checked it but it was completely broken.',\n'label': 'Stephen knocked over a vase while drunk.'\n}",
-      "structure-splits": "- train: Consists of training instances. \n- dev: Consists of dev instances.\n- test: Consists of test instances.\n"
+      "structure-example": "```\n{\n'gem_id': 'GEM-ART-validation-0',\n'observation_1': 'Stephen was at a party.',\n'observation_2': 'He checked it but it was completely broken.',\n'label': 'Stephen knocked over a vase while drunk.'\n}\n```",
+      "structure-splits": "- `train`: Consists of training instances. \n- `dev`: Consists of dev instances.\n- `test`: Consists of test instances.\n"
+    },
+    "what": {
+      "dataset": "Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation.\nThis data loader focuses on abductive NLG: a conditional English generation task for explaining given observations in natural language. "
     }
   },
   "gem": {