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.

[Q]:  Recurrent Neural Network (RNN) Language Models
are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. ActionLSTM
models the linearized bracketed tree structure of a sentence by learning to predict the next action required to construct a phrase-structure parse BIBREF16. Generative Recurrent Neural Network Grammars (RNNG)
jointly model the word sequence as well as the underlying syntactic structure BIBREF18. 
 Question: What are the baseline models?
[A]: Recurrent Neural Network (RNN) ActionLSTM Generative Recurrent Neural Network Grammars (RNNG)


[Q]: Encoders with induced latent structures have been shown to benefit several tasks including document classification, natural language inference BIBREF12, BIBREF13, and machine translation BIBREF11.  
 Question: Is there any evidence that encoders with latent structures work well on other tasks?
[A]: Yes


[Q]: To overcome the size issue of the student reflection dataset, we first explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PG-net)BIBREF0.  
 Question: What is the recent abstractive summarization method in this paper?
[A]:
pointer networks with coverage mechanism (PG-net)