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:
We build on the state-of-the-art publicly available question answering system by docqa. The system extends BiDAF BIBREF4 with self-attention and performs well on document-level QA.  
 Question: What is the underlying question answering algorithm?

Ex Output:
The system extends BiDAF BIBREF4 with self-attention


Ex Input:
Cloze Track User Query Track 
 Question: What two types the Chinese reading comprehension dataset consists of?

Ex Output:
cloze-style reading comprehension and user query reading comprehension questions


Ex Input:
As our models did not use RL refinement, these results constitute significant gains of 1.34 and 1.12 BLEU score on top of the strong baselines in BIBREF3 . The perplexity scores are also better. On the Google Production dataset, our model achieved 1.01 higher test BLEU score even after training for only one sixth of the time. 
 Question: What improvement does the MOE model make over the SOTA on machine translation?

Ex Output:

1.34 and 1.12 BLEU score on top of the strong baselines in BIBREF3 perplexity scores are also better On the Google Production dataset, our model achieved 1.01 higher test BLEU score