@inproceedings{kondadadi-etal-2022-data,
    title = "Data Quality Estimation Framework for Faster Tax Code Classification",
    author = "Kondadadi, Ravi  and
      Williams, Allen  and
      Nicolov, Nicolas",
    editor = "Malmasi, Shervin  and
      Rokhlenko, Oleg  and
      Ueffing, Nicola  and
      Guy, Ido  and
      Agichtein, Eugene  and
      Kallumadi, Surya",
    booktitle = "Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.ecnlp-1.4/",
    doi = "10.18653/v1/2022.ecnlp-1.4",
    pages = "29--34",
    abstract = "This paper describes a novel framework to estimate the data quality of a collection of product descriptions to identify required relevant information for accurate product listing classification for tax-code assignment. Our Data Quality Estimation (DQE) framework consists of a Question Answering (QA) based attribute value extraction model to identify missing attributes and a classification model to identify bad quality records. We show that our framework can accurately predict the quality of product descriptions. In addition to identifying low-quality product listings, our framework can also generate a detailed report at a category level showing missing product information resulting in a better customer experience."
}