instruction:
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.
question:
As previous systems collect relevant data from knowledge bases after observing questions during evaluation BIBREF24 , BIBREF25 , we also explore using this option. Namely, we build a customized text corpus based on questions in commonsense reasoning tasks.  
 Question: Do they fine-tune their model on the end task?
answer:
Yes


question:
We use OpenNMT BIBREF24 as the implementation of the NMT system for all experiments BIBREF5 . PBMT-R is a phrase-based method with a reranking post-processing step BIBREF18 . Hybrid performs sentence splitting and deletion operations based on discourse representation structures, and then simplifies sentences with PBMT-R BIBREF25 . SBMT-SARI BIBREF19 is syntax-based translation model using PPDB paraphrase database BIBREF26 and modifies tuning function (using SARI). Dress is an encoder-decoder model coupled with a deep reinforcement learning framework, and the parameters are chosen according to the original paper BIBREF20 . 
 Question: what state of the art methods did they compare with?
answer:
OpenNMT PBMT-R Hybrid SBMT-SARI Dress


question:
69.10%/78.38% 
 Question: how much of improvement the adaptation model can get?
answer:

 69.10%/78.38%