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.

Ex 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?

Ex Output:
our Open model achieves more than 3 points of f1-score than the state-of-the-art result


Ex Input:
The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large literature on this question (including the aforementioned language modeling, unsupervised parsing, and alignment work), but in the current work we will make use of a very simple criterion: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same. 
 Question: How do they determine similar environments for fragments in their data augmentation scheme?

Ex Output:
fragments are interchangeable if they occur in at least one lexical environment that is exactly the same


Ex Input:
Our model BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS outperforms all other model experimented in OurNepali and ILPRL dataset respectively. 
 Question: What is the best model?

Ex Output:

BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS