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Increasing Naturalness and Flexibility in Spoken Dialogue Interaction pp 277–289Cite as

A Differentiable Generative Adversarial Network for Open Domain Dialogue

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 714)

Abstract

This work presents a novel methodology to train open domain neural dialogue systems within the framework of Generative Adversarial Networks with gradient based optimization methods. We avoid the non-differentiability related to text-generating networks approximating the word vector corresponding to each generated token via a top-k softmax. We show that a weighted average of the word vectors of the most probable tokens computed from the probabilities resulting of the top-k softmax leads to a good approximation of the word vector of the generated token. Finally we demonstrate through a human evaluation process that training a neural dialogue system via adversarial learning with this method successfully discourages it from producing generic responses. Instead it tends to produce more informative and variate ones.

Keywords

  • Dialogue systems
  • Generative adversarial networks
  • Open domain dialogue

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Acknowledgements

This work has been partially funded by the Basque Government under grant PRE_2017_1_0357, by the University of the Basque Country UPV/EHU under grant PIF17/310, and by the H2020 RIA EMPATHIC (Grant N: 769872).

Author information

Authors and Affiliations

  1. Faculty of Science and Technology, University of the Basque Country UPV/EHU, Sarriena s/n, 48940, Leioa, Spain

    Asier López Zorrilla, Mikel deVelasco Vázquez & M. Inés Torres

Authors
  1. Asier López Zorrilla
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  2. Mikel deVelasco Vázquez
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  3. M. Inés Torres
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Corresponding author

Correspondence to Asier López Zorrilla .

Editor information

Editors and Affiliations

  1. Apple, Cupertino, CA, USA

    Dr. Erik Marchi

  2. Kore University of Enna, Enna, Italy

    Prof. Dr. Sabato Marco Siniscalchi

  3. Polytechnic University of Turin, Torino, Italy

    Dr. Sandro Cumani

  4. Kore University of Enna, Enna, Italy

    Prof. Dr. Valerio Mario Salerno

  5. National University of Singapore, Singapore, Singapore

    Prof. Dr. Haizhou Li

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López Zorrilla, A., deVelasco Vázquez, M., Torres, M.I. (2021). A Differentiable Generative Adversarial Network for Open Domain Dialogue. In: Marchi, E., Siniscalchi, S.M., Cumani, S., Salerno, V.M., Li, H. (eds) Increasing Naturalness and Flexibility in Spoken Dialogue Interaction. Lecture Notes in Electrical Engineering, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-15-9323-9_24

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  • DOI: https://doi.org/10.1007/978-981-15-9323-9_24

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