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.

Example Input: The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25).  
 Question: Which five natural disasters were examined?
Example Output: the East Coast Bomb Cyclone  the Mendocino, California wildfires Hurricane Florence Hurricane Michael the California Camp Fires

Example Input: We only used models of dimension 300 and, for Word2Vec, Wang2Vec, and FastText, skip-gram architectured models. 
 Question: what word embedding techniques did they experiment with?
Example Output: Word2Vec, Wang2Vec, and FastText

Example Input:  The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks. The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks. 
 Question: What are the relative improvements observed over existing methods?
Example Output:
The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks.

