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Pat-in-the-Loop : Declarative Knowledge for Controlling Neural Networks

Author

Listed:
  • Dario Onorati

    (Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Roma, Italy)

  • Pierfrancesco Tommasino

    (Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Roma, Italy)

  • Leonardo Ranaldi

    (Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy)

  • Francesca Fallucchi

    (Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy)

  • Fabio Massimo Zanzotto

    (Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Roma, Italy)

Abstract

The dazzling success of neural networks over natural language processing systems is imposing an urgent need to control their behavior with simpler, more direct declarative rules. In this paper, we propose Pat-in-the-Loop as a model to control a specific class of syntax-oriented neural networks by adding declarative rules. In Pat-in-the-Loop, distributed tree encoders allow to exploit parse trees in neural networks, heat parse trees visualize activation of parse trees, and parse subtrees are used as declarative rules in the neural network. Hence, Pat-in-the-Loop is a model to include human control in specific natural language processing (NLP)-neural network (NN) systems that exploit syntactic information, which we will generically call Pat. A pilot study on question classification showed that declarative rules representing human knowledge, injected by Pat, can be effectively used in these neural networks to ensure correctness, relevance, and cost-effective.

Suggested Citation

  • Dario Onorati & Pierfrancesco Tommasino & Leonardo Ranaldi & Francesca Fallucchi & Fabio Massimo Zanzotto, 2020. "Pat-in-the-Loop : Declarative Knowledge for Controlling Neural Networks," Future Internet, MDPI, vol. 12(12), pages 1-12, December.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:218-:d:454952
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    References listed on IDEAS

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    1. Rachel Courtland, 2018. "Bias detectives: the researchers striving to make algorithms fair," Nature, Nature, vol. 558(7710), pages 357-360, June.
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