| Challenge: | Existing models that understand search intent are difficult to learn due to lack of labeled datasets. |
| Approach: | They develop a deep, end-to-end model that learns to effectively classify mismatches . they introduce a latent variable into the cross-entropy loss that alternates between real and generated samples . |
| Outcome: | The proposed model achieves a relative gain of over 26% in F-score and 17% in Area Under PR curve on live search traffic in multiple countries. |
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