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International Conference of the Pacific Association for Computational Linguistics

PACLING 2019: Computational Linguistics pp 231–242Cite as

Automatic Approval of Online Comments with Multiple-Encoder Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1215))

Abstract

In modern online publishing, user comments are an integral part of any media platform. Between the high volume of generated comments and the need for moderation of inappropriate content, human approval becomes a serious bottleneck with negative consequences for both operating cost and user experience. To alleviate this problem we present a text classification model for automatic approval of user comments on text articles. With multiple textual input from both the comment in question and the host article, the model uses a neural network with multiple encoders. Different choices for encoder networks and combination methods for encoder outputs are investigated. The system is evaluated on news articles from a leading Vietnamese online media provider, and is currently on a test run with said newspaper.

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Author information

Authors and Affiliations

  1. FPT Technology Research Institute, FPT University, Hanoi, Vietnam

    Vu Dang

Authors
  1. Vu Dang
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Correspondence to Vu Dang .

Editor information

Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Le-Minh Nguyen

  2. University of Engineering and Technology, Hanoi, Vietnam

    Xuan-Hieu Phan

  3. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Kôiti Hasida

  4. Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Satoshi Tojo

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Dang, V. (2020). Automatic Approval of Online Comments with Multiple-Encoder Networks. In: Nguyen, LM., Phan, XH., Hasida, K., Tojo, S. (eds) Computational Linguistics. PACLING 2019. Communications in Computer and Information Science, vol 1215. Springer, Singapore. https://doi.org/10.1007/978-981-15-6168-9_20

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  • DOI: https://doi.org/10.1007/978-981-15-6168-9_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6167-2

  • Online ISBN: 978-981-15-6168-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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