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.
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Question: 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?

Answer: current model

Question: Table TABREF14 show the results obtained for proposed s2sL approach in comparison to that of MLP for the tasks of Speech/Music and Neutral/Sad classification, by considering different proportions of training data.
Question: Which models/frameworks do they compare to?

Answer: MLP

Question: We started from an initial corpus that did not have revisions, and kept adding new documents and revising existing documents. The revision operations we used were deletion, addition and replacement of words, sentences, paragraphs, section names and document titles. The addition of words, ..., section names, and new documents were pulled from the Wikipedia abstracts. We generated six data sets using different random seeds, and each data set contained six corpora (Corpus 0 - 5).
Question: What are simulated datasets collected?

Answer:
There are 6 simulated datasets collected which is initialised with a corpus of size 550 and simulated by generating new documents from Wikipedia extracts and replacing existing documents