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: Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback. 
 Question: How are workers trained?
Example Output: extensive personal feedback

Example Input: This kind of error affects the precision and recall for the “troll” and “engage” classes. A solution to this problem may be the inclusion of longer parts of the conversation.  
 Question: What potential solutions are suggested?
Example Output:  inclusion of longer parts of the conversation

Example Input: In this paper, we introduce a novel policy model to output multiple actions per turn (called multi-act), generating a sequence of tuples and expanding agents' expressive power. Each tuple is defined as $(\textit {continue}, \textit {act}, \textit {slots})$, where continue indicates whether to continue or stop producing new acts, act is an act type (e.g., inform or request), and slots is a set of slots (names) associated with the current act type. Correspondingly, a novel decoder (Figure FIGREF5) is proposed to produce such sequences. Each tuple is generated by a cell called gated Continue Act Slots (gCAS, as in Figure FIGREF7), which is composed of three sequentially connected gated units handling the three components of the tuple. This decoder can generate multi-acts in a double recurrent manner BIBREF18.  
 Question: What is specific to gCAS cell?
Example Output:
It has three sequentially connected units to output continue, act and slots generating multi-acts in a doble recurrent manner.