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.

Onorati, D., Tommasino, P., Ranaldi, L., Fallucchi, F., Zanzotto, F.m. (2020). Pat-in-the-loop: Declarative knowledge for controlling neural networks. FUTURE INTERNET, 12(12), 1-12 [10.3390/fi12120218].

Pat-in-the-loop: Declarative knowledge for controlling neural networks

Zanzotto F. M.
2020-01-01

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.
2020
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore INF/01 - INFORMATICA
Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
English
AI
Deep learning
Human-in-the-loop
Machine learning
NLP
Onorati, D., Tommasino, P., Ranaldi, L., Fallucchi, F., Zanzotto, F.m. (2020). Pat-in-the-loop: Declarative knowledge for controlling neural networks. FUTURE INTERNET, 12(12), 1-12 [10.3390/fi12120218].
Onorati, D; Tommasino, P; Ranaldi, L; Fallucchi, F; Zanzotto, Fm
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/298915
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