  Showing items that do not match search query intent degrades customer
experience in e-commerce. These mismatches result from counterfactual biases of
the ranking algorithms toward noisy behavioral signals such as clicks and
purchases in the search logs. Mitigating the problem requires a large labeled
dataset, which is expensive and time-consuming to obtain. In this paper, we
develop a deep, end-to-end model that learns to effectively classify mismatches
and to generate hard mismatched examples to improve the classifier. We train
the model end-to-end by introducing a latent variable into the cross-entropy
loss that alternates between using the real and generated samples. This not
only makes the classifier more robust but also boosts the overall ranking
performance. Our model achieves a relative gain compared to baselines by over
26% in F-score, and over 17% in Area Under PR curve. On live search traffic,
our model gains significant improvement in multiple countries.
