In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: DNN based models coupled with transfer learning beat the best-known results for all three datasets. Previous best F1 scores for Wikipedia BIBREF4 and Twitter BIBREF8 datasets were 0.68 and 0.93 respectively. We achieve F1 scores of 0.94 for both these datasets using BLSTM with attention and feature level transfer learning (Table TABREF25 ). For Formspring dataset, authors have not reported F1 score. Their method has accuracy score of 78.5% BIBREF2 . We achieve F1 score of 0.95 with accuracy score of 98% for the same dataset.

Output: What were their performance results?


Input: Consider Input: We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities.

Output: What annotations are available in the dataset - tweat used hate speach or not?


Input: Consider Input: For all the tasks in our experimental study, we use 36 millions English tweets collected between August and September 2017. 


Output: Do they report results only on English datasets?