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.

Example Input: Machine-machine Interaction A related line of work explores simulation-based dialogue generation, where the user and system roles are simulated to generate a complete conversation flow, which can then be converted to natural language using crowd workers BIBREF1. Such a framework may be cost-effective and error-resistant since the underlying crowd worker task is simpler, and semantic annotations are obtained automatically. It is often argued that simulation-based data collection does not yield natural dialogues or sufficient coverage, when compared to other approaches such as Wizard-of-Oz. We argue that simulation-based collection is a better alternative for collecting datasets like this owing to the factors below. 
 Question: How did they gather the data?
Example Output: simulation-based dialogue generation, where the user and system roles are simulated to generate a complete conversation flow, which can then be converted to natural language using crowd workers 

Example Input: Figure FIGREF9 illustrates the overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer. Embedding layer has two types: GloVe embedding and BERT embedding. Accordingly, the models are named AEN-GloVe and AEN-BERT. 
 Question: How is their model different from BERT?
Example Output: overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer.

Example Input: Information Gathering: At step $t$ of the information gathering phase, the agent can issue one of the following four actions to interact with the paragraph $p$, where $p$ consists of $n$ sentences and where the current observation corresponds to sentence $s_k,~1 \le k \le n$:

previous: jump to $ \small {\left\lbrace \begin{array}{ll} s_n & \text{if $k = 1$,}\\ s_{k-1} & \text{otherwise;} \end{array}\right.} $

next: jump to $ \small {\left\lbrace \begin{array}{ll} s_1 & \text{if $k = n$,}\\ s_{k+1} & \text{otherwise;} \end{array}\right.} $

Ctrl+F $<$query$>$: jump to the sentence that contains the next occurrence of “query”;

stop: terminate information gathering phase. 
 Question: What commands does their setup provide to models seeking information?
Example Output:
previous next Ctrl+F $<$query$>$ stop