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: The library design of Torch-Struct follows the distributions API used by both TensorFlow and PyTorch BIBREF29. 
 Question: Does API provide ability to connect to models written in some other deep learning framework?
Example Output: Yes

Example Input: To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class. 
 Question: How is "hirability" defined?
Example Output: candidates who have been liked or shortlisted are considered part of the hirable class

Example Input: (2) Different from LEM and DPEMM, AEM uses a generator network to capture the event-related patterns and is able to mine events from different text sources (short and long). Moreover, unlike traditional inference procedure, such as Gibbs sampling used in LEM and DPEMM, AEM could extract the events more efficiently due to the CUDA acceleration; 
 Question: What alternative to Gibbs sampling is used?
Example Output:
generator network to capture the event-related patterns