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 choice of two methods for our empirical study is motivated by the best performance achieved by Logistic Regression in question-question similarity at SemEval 2017 (best system BIBREF37 and second best system BIBREF38 ), and the high performance achieved by neural networks on larger datasets such as SNLI BIBREF13 , BIBREF39 , BIBREF40 , BIBREF41  
 Question: What machine learning and deep learning methods are used for RQE?
Example Output: Logistic Regression neural networks

Example Input: I first measured the effectiveness of the embeddings at the skip-gram's training task, predicting context words INLINEFORM0 given input words INLINEFORM1 . This task measures the methods' performance for predictive language modeling. I used four datasets of sociopolitical, scientific, and literary interest: the corpus of NIPS articles from 1987 – 1999 ( INLINEFORM2 million), the U.S. presidential state of the Union addresses from 1790 – 2015 ( INLINEFORM3 ), the complete works of Shakespeare ( INLINEFORM4 ; this version did not contain the Sonnets), and the writings of black scholar and activist W.E.B. Du Bois, as digitized by Project Gutenberg ( INLINEFORM5 ). For each dataset, I held out 10,000 INLINEFORM6 pairs uniformly at random, where INLINEFORM7 , and aimed to predict INLINEFORM8 given INLINEFORM9 (and optionally, INLINEFORM10 ). Since there are a large number of classes, I treat this as a ranking problem, and report the mean reciprocal rank. The experiments were repeated and averaged over 5 train/test splits. 
 Question: What is MRR?
Example Output: mean reciprocal rank

Example Input: For the evaluation of performance of the proposed method on the NLI task, SNLI BIBREF22 and MultiNLI BIBREF23 datasets are used. We use Quora Question Pairs dataset BIBREF24 in evaluating the performance of our method on the PI task. In evaluating sentiment classification performance, the Stanford Sentiment Treebank (SST) BIBREF25 is used. 
 Question: Which datasets were used?
Example Output:
SNLI BIBREF22 and MultiNLI BIBREF23 Quora Question Pairs dataset BIBREF24  Stanford Sentiment Treebank (SST) BIBREF25