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 Q]:  Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).  
 Question: Which machine learning models are used?
[EX A]: RNNs CNNs Naive Bayes with Laplace Smoothing k-clustering SVM with linear kernel

[EX Q]: 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?
[EX A]: OpenNMT PBMT-R Hybrid SBMT-SARI Dress

[EX Q]: In this paper, we propose three different types of CRU models: shallow fusion, deep fusion and deep-enhanced fusion, from the most fundamental one to the most expressive one. The most simple one is to directly apply a CNN layer after the embedding layer to obtain blended contextual representations. Then a GRU layer is applied afterward. 
 Question: How is CNN injected into recurent units?
[EX A]:
The most simple one is to directly apply a CNN layer after the embedding layer to obtain blended contextual representations. Then a GRU layer is applied afterward.