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:
Hypothesis 4 (H4) : The proposed template-based synthesis model outperforms a simple word replacement model. Table TABREF13 supports H4 by showing that the proposed synthesis model outperforms the WordNet baseline in training (rows 7, 8 and 19, 20) except Stat2016, and tuning (10, 11 and 22, 23) over all courses. It also shows that while adding synthetic data from the baseline is not always helpful, adding synthetic data from the template model helps to improve both the training and the tuning process. In both CS and ENGR courses, tuning with synthetic data enhances all ROUGE scores compared to tuning with only the original data. (rows 9 and 11). As for Stat2015, R-1 and R-$L$ improved, while R-2 decreased. For Stat2016, R-2 and R-$L$ improved, and R-1 decreased (rows 21 and 23). Training with both student reflection data and synthetic data compared to training with only student reflection data yields similar improvements, supporting H3 (rows 6, 8 and 18, 20). 
 Question: Is the template-based model realistic?  
answer:
Yes


question:
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?
answer:
The system extends BiDAF BIBREF4 with self-attention


question:
Second, we provide an extensive analysis of the state-of-the-art model for the NLI task and show that our methods reveal interesting insights not available from traditional methods of inspecting attention and word saliency. 
 Question: Did they use the state-of-the-art model to analyze the attention?
answer:

we provide an extensive analysis of the state-of-the-art model