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An Enhanced Convolutional Inference Model with Distillation for Retrieval-Based QA

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Database Systems for Advanced Applications (DASFAA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12683))

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Abstract

A common solution of automatic question-answering (QA) systems is retrieving the most similar question for a given user query from a QA knowledge base. Even though some models have got promising performance on this task, it may be hard for them to achieve a balance between accuracy and efficiency. In this paper, we propose an enhanced convolutional inference model with StructBert distillation, called StructBert-ECIM, to achieve such balance.

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References

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Authors and Affiliations

  1. Alibaba Groups, Hangzhou, 311121, China

    Shuangyong Song, Chao Wang, Xiao Pu, Zehui Wang & Huan Chen

Authors
  1. Shuangyong Song
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  2. Chao Wang
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  3. Xiao Pu
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  4. Zehui Wang
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  5. Huan Chen
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Corresponding author

Correspondence to Shuangyong Song .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

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Song, S., Wang, C., Pu, X., Wang, Z., Chen, H. (2021). An Enhanced Convolutional Inference Model with Distillation for Retrieval-Based QA. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_35

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  • DOI: https://doi.org/10.1007/978-3-030-73200-4_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73199-1

  • Online ISBN: 978-3-030-73200-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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