  Recent advances in Generative Adversarial Networks (GANs) have resulted in
its widespread applications to multiple domains. A recent model, IRGAN, applies
this framework to Information Retrieval (IR) and has gained significant
attention over the last few years. In this focused work, we critically analyze
multiple components of IRGAN, while providing experimental and theoretical
evidence of some of its shortcomings. Specifically, we identify issues with the
constant baseline term in the policy gradients optimization and show that the
generator harms IRGAN's performance. Motivated by our findings, we propose two
models influenced by self-contrastive estimation and co-training which
outperform IRGAN on two out of the three tasks considered.

Machine Learning;Computation and Language;Information Retrieval