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Meaningful Clusterings of Recurrent Neural Network Activations for NLP

  • Mihai PomarlanEmail author
  • John Bateman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11308)

Abstract

Recurrent neural networks have found applications in NLP, but their operation is difficult to interpret. A state automaton that approximates the network would be more interpretable, but for this one needs a method to group network activation states by their behavior. In this paper we propose such a method, and compare it to an existing dimensionality reduction and clustering approach. Our method is better able to group together neural states of similar behavior.

Keywords

Recurrent neural networks Natural language processing Interpretability 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of BremenBremenGermany

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