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Is the discriminator's goal to determine relevant vs. non-relevant query-document pairs, or true vs. generated query-document pairs? If the former is true, shouldn't the stopping criteria for IRGAN be the discriminator converging to 1 (relevant) everywhere, instead of 0.5? If the latter is true, shouldn't the generator perform positive sampling instead of negative sampling in order to fool the discriminator? Thanks in advance for the clarification!
The text was updated successfully, but these errors were encountered:
The discriminator tries to detect whether a query-doc pair is from true data distribution or the generated one, not about the (non)-relevancy. The generator tries to create a CONDITIONAL distribution of documents given the user's query P(d|q;theta), thus when such a distribution perfectly fits the true preference distribution P_data(d|q), the discriminator cannot distinguish whether a query-doc pair is from P(d|q;theta) or P_data(d|q). Thus the prediction of D should be 0.5 instead of 1.0.
Is the discriminator's goal to determine relevant vs. non-relevant query-document pairs, or true vs. generated query-document pairs? If the former is true, shouldn't the stopping criteria for IRGAN be the discriminator converging to 1 (relevant) everywhere, instead of 0.5? If the latter is true, shouldn't the generator perform positive sampling instead of negative sampling in order to fool the discriminator? Thanks in advance for the clarification!
The text was updated successfully, but these errors were encountered: