Add information to dataset card (#5)

- Add information to dataset card (9d2c9054c678acaf5eb7cfca22e6bce139cf3ce8)
- Delete empty information (4c4de5853b1510957e4a10e7aa2534f0975d217e)

README.md

@@ -5,8 +5,7 @@ language_creators:
 - crowdsourced
 language:
 - en
-license:
-- unknown
+license: odc-by
 multilinguality:
 - monolingual
 size_categories:
@@ -282,14 +281,16 @@ configs:
 ## Dataset Description
 
 - **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]()
-- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
-- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
-- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
-- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
+- **Repository:** https://github.com/Websail-NU/CODAH
+- **Paper:** https://aclanthology.org/W19-2008/
+- **Paper:** https://arxiv.org/abs/1904.04365
 
 ### Dataset Summary
 
-[More Information Needed]
+The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense
+question-answering in the sentence completion style of SWAG. As opposed to other automatically generated
+NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model
+and use this information to design challenging commonsense questions.
 
 ### Supported Tasks and Leaderboards
 
@@ -368,11 +369,33 @@ configs:
 
 ### Licensing Information
 
-[More Information Needed]
+The CODAH dataset is made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/
 
 ### Citation Information
 
-[More Information Needed]
+```
+@inproceedings{chen-etal-2019-codah,
+    title = "{CODAH}: An Adversarially-Authored Question Answering Dataset for Common Sense",
+    author = "Chen, Michael  and
+      D{'}Arcy, Mike  and
+      Liu, Alisa  and
+      Fernandez, Jared  and
+      Downey, Doug",
+    editor = "Rogers, Anna  and
+      Drozd, Aleksandr  and
+      Rumshisky, Anna  and
+      Goldberg, Yoav",
+    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
+    month = jun,
+    year = "2019",
+    address = "Minneapolis, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W19-2008",
+    doi = "10.18653/v1/W19-2008",
+    pages = "63--69",
+    abstract = "Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense, but are able to exploit limitations of the datasets to achieve human-level scores. We introduce the CODAH dataset, an adversarially-constructed evaluation dataset for testing common sense. CODAH forms a challenging extension to the recently-proposed SWAG dataset, which tests commonsense knowledge using sentence-completion questions that describe situations observed in video. To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems. Workers are rewarded for submissions that models fail to answer correctly both before and after fine-tuning (in cross-validation). We create 2.8k questions via this procedure and evaluate the performance of multiple state-of-the-art question answering systems on our dataset. We observe a significant gap between human performance, which is 95.3{\%}, and the performance of the best baseline accuracy of 65.3{\%} by the OpenAI GPT model.",
+}
+```
 
 ### Contributions