@inproceedings{joshi-etal-2022-augmenting,
    title = "Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic {E}-commerce Stores",
    author = "Joshi, Ashutosh  and
      Vishwanath, Shankar  and
      Teo, Choon  and
      Petricek, Vaclav  and
      Vishwanathan, Vishy  and
      Bhagat, Rahul  and
      May, Jonathan",
    editor = "Loukina, Anastassia  and
      Gangadharaiah, Rashmi  and
      Min, Bonan",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
    month = jul,
    year = "2022",
    address = "Hybrid: Seattle, Washington + Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2022.naacl-industry.19/",
    doi = "10.18653/v1/2022.naacl-industry.19",
    pages = "160--167",
    abstract = "Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amounts of customer behavior data (e.g. queries, clicks, purchases) for training. However, behavioral data is limited in low-traffic e-commerce stores, impacting performance of these systems. In this paper, we present a technique that augments behavioral training data via query reformulation. We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation. We also show in online A/B tests that our method significantly improves business metrics for the AL-XMC model."
}