added PubMedQA data

README.md

@@ -192,10 +192,13 @@ The sentence pairs were evaluated by five different human experts that judged th
 
 
 #### PubMedQA
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
+
+We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement. PubMedQA is publicly available at this https URL.
+
+- **Homepage:** https://pubmedqa.github.io/
+- **Repository:** https://github.com/pubmedqa/pubmedqa
+- **Paper:** [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/pdf/1909.06146.pdf)
+- **Leaderboard:** [Question answering](https://pubmedqa.github.io/)
 - **Point of Contact:**
 
 #### BioASQ
@@ -446,7 +449,16 @@ All the datasets have been obtained and annotated by experts in thebiomedical do
                     pages={i49--i58},
                     year={2017},
                     publisher={Oxford University Press}
-                  }"""
+                  }""",
+                  
+       "PubMedQA":"""@inproceedings{jin2019pubmedqa,
+                        title={PubMedQA: A Dataset for Biomedical Research Question Answering},
+                        author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
+                        booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
+                        pages={2567--2577},
+                        year={2019}
+                      }
+                             """
                     
   }
   ```