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: Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings. 
 Question: How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?
Example Output: our Open model achieves more than 3 points of f1-score than the state-of-the-art result

Example Input: To collect a diverse training dataset, we have randomly sampled 1000 posts each from the subreddits politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 
 Question: what are the topics pulled from Reddit?
Example Output: politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 

Example Input:  Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability. 
 Question: Where does the data in CoLA come from?
Example Output:
 CoLA contains example sentences from linguistics publications labeled by experts