In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Input: Consider Input: To show the effectiveness of our approach, we show results on the SICK dataset BIBREF1, a common benchmark for logic-based NLI, and find MonaLog to be competitive with more complicated logic-based approaches (many of which require full semantic parsing and more complex logical machinery). 
 Question: Do they beat current state-of-the-art on SICK?

Output: No


Input: Consider Input: The input for both tasks consists of news articles in free-text format, collected from 36 propagandist and 12 non-propagandist news outlets and then annotated by professional annotators.  
 Question: What dataset was used?

Output:  news articles in free-text format


Input: Consider Input: Most of the above-discussed systems either shows high performance on (a) Twitter dataset or (b) Facebook dataset (given in the TRAC-2018), but not on both English code-mixed datasets. This may be due to the text style or level of complexities of both datasets. 
 Question: How have the differences in communication styles between Twitter and Facebook increase the complexity of the problem?


Output: Systems do not perform well both in Facebook and Twitter texts