@inproceedings{liu-etal-2018-semi,
    title = "Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification",
    author = "Liu, Mingkuan  and
      Wen, Musen  and
      Kopru, Selcuk  and
      Liu, Xianjing  and
      Lu, Alan",
    editor = "Haffari, Reza  and
      Cherry, Colin  and
      Foster, George  and
      Khadivi, Shahram  and
      Salehi, Bahar",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-3409/",
    doi = "10.18653/v1/W18-3409",
    pages = "68--76",
    abstract = "The lack of high-quality labeled training data has been one of the critical challenges facing many industrial machine learning tasks. To tackle this challenge, in this paper, we propose a semi-supervised learning method to utilize unlabeled data and user feedback signals to improve the performance of ML models. The method employs a primary model Main and an auxiliary evaluation model Eval, where Main and Eval models are trained iteratively by automatically generating labeled data from unlabeled data and/or users' feedback signals. The proposed approach is applied to different text classification tasks. We report results on both the publicly available Yahoo! Answers dataset and our e-commerce product classification dataset. The experimental results show that the proposed method reduces the classification error rate by 4{\%} and up to 15{\%} across various experimental setups and datasets. A detailed comparison with other semi-supervised learning approaches is also presented later in the paper. The results from various text classification tasks demonstrate that our method outperforms those developed in previous related studies."
}