@inproceedings{nguyen-etal-2020-learning,
    title = "Learning Robust Models for e-Commerce Product Search",
    author = "Nguyen, Thanh  and
      Rao, Nikhil  and
      Subbian, Karthik",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.acl-main.614/",
    doi = "10.18653/v1/2020.acl-main.614",
    pages = "6861--6869",
    abstract = "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."
}