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        <title>Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification
%A Liu, Mingkuan
%A Wen, Musen
%A Kopru, Selcuk
%A Liu, Xianjing
%A Lu, Alan
%Y Haffari, Reza
%Y Cherry, Colin
%Y Foster, George
%Y Khadivi, Shahram
%Y Salehi, Bahar
%S Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne
%F liu-etal-2018-semi
%X 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.
%R 10.18653/v1/W18-3409
%U https://aclanthology.org/W18-3409
%U https://doi.org/10.18653/v1/W18-3409
%P 68-76

Markdown (Informal)

[Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification](https://aclanthology.org/W18-3409) (Liu et al., ACL 2018)

• Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification (Liu et al., ACL 2018)

ACL

• Mingkuan Liu, Musen Wen, Selcuk Kopru, Xianjing Liu, and Alan Lu. 2018. Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pages 68–76, Melbourne. Association for Computational Linguistics.