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: As a framework, we use the sentence classifier configuration of FLAIR BIBREF46 with a biLSTM encoder/classifier architecture fed by character and word level representations composed of a concatenation of fixed 300 dimensional GloVe embeddings BIBREF47, pre-trained contextualized FLAIR word embeddings, and pre-trained contextualized character embeddings from AllenNLP BIBREF48 with FLAIR's default hyperparameters. 
 Question: Are some models evaluated using this metric, what are the findings?
Example Output: Yes

Example Input: In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events. Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing. 
 Question: Which of their training domains improves performance the most?
Example Output: documents from the CommonCrawl dataset that has the most overlapping n-grams with the question

Example Input: We analyze which discourse phenomena are hard to capture using monolingual data only. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations. 
 Question: what phenomena do they mention is hard to capture?
Example Output:
Four discourse phenomena - deixis, lexical cohesion, VP ellipsis, and ellipsis which affects NP inflection.