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: 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?
Example Output: OpenNMT PBMT-R Hybrid SBMT-SARI Dress

Example Input: The contributions of this work are: 
 Question: Do they look for inconsistencies between different UD treebanks?
Example Output: Yes

Example Input: In this work, the experiments are conducted using the BABEL speech corpus collected from the IARPA babel program.  
 Question: What data do they train the language models on?
Example Output:
 BABEL speech corpus