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]: We defined the reward as being 1 for successfully completing the task, and 0 otherwise. A discount of $0.95$ was used to incentivize the system to complete dialogs faster rather than slower, yielding return 0 for failed dialogs, and $G = 0.95^{T-1}$ for successful dialogs, where $T$ is the number of system turns in the dialog.   0.95^{T-1} reward  0.95^{T-1}  
 Question: What is the reward model for the reinforcement learning appraoch?
[A]: reward 1 for successfully completing the task, with a discount by the number of turns, and reward 0 when fail


[Q]: Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence. 
 Question: Do they only use adjacent entity mentions or use more than that in some cases (next to adjacent)?
[A]: NCEL considers only adjacent mentions.


[Q]: In Sec. "Materials and Methods" we discuss our materials and methods, including the dataset we studied, how we preprocessed that data and extracted a `causal' corpus and a corresponding `control' corpus, and the details of the statistical and language analysis tools we studied these corpora with. 
 Question: Do they use expert annotations, crowdsourcing, or only automatic methods to analyze the corpora?
[A]:
Only automatic methods


