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:
In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST).  
 Question: What discourse features are used?
answer:
Entity grid with grammatical relations and RST discourse relations.


question:
We empirically test our approach on a series of experiments on WikiTableQuestions, to our knowledge the only dataset designed for this task. In Algorithm 1 we describe how logical forms are transformed into interpretable textual representations called "paraphrases". 
 Question: What is the source of the paraphrases of the questions?
answer:
WikiTableQuestions


question:
The seq2seq model with global attention gives the best results with an average target BLEU score of 29.65 on the style transfer dataset, compared with an average target BLEU score of 26.97 using the seq2seq model with pointer networks. 
 Question: What is best BLEU score of language style transfer authors got?
answer:

seq2seq model with global attention gives the best results with an average target BLEU score of 29.65