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: At each training step, we train the model with a batch of original examples along with adversarial examples with balanced labels. The adversarial examples account for around 10% in a batch. During training, we generate adversarial examples with the current model as the target and update the model parameters with the hybrid batch iteratively.
Question: What is discriminator in this generative adversarial setup?

A: current model
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Q: (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?

A: generator network to capture the event-related patterns
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Q: Our dataset contains tweets about 'ObamaCare' in USA collected during march 2010.
Question: What dataset of tweets is used?

A:
tweets about 'ObamaCare' in USA collected during march 2010
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