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.

[EX 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?
[EX A]:  current model

[EX Q]: We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. 
 Question: What unimodal detection models were used?
[EX A]:  single layer LSTM with a 150-dimensional hidden state for hate / not hate classification

[EX Q]: The baseline model BIBREF3 is implemented with a recurrent neural network based encoder-decoder framework. 
 Question: Do they compare against Noraset et al. 2017?
[EX A]:
Yes