Adversarial Learning on the Latent Space for Diverse Dialog Generation

Kashif Khan; Gaurav Sahu; Vikash Balasubramanian; Lili Mou; Olga Vechtomova

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arXiv · 1911.03817

Adversarial Learning on the Latent Space for Diverse Dialog Generation

Kashif Khan·Gaurav Sahu·Vikash Balasubramanian·Lili Mou·Olga Vechtomova

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Abstract

Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns a meaningful representation of sentences by autoencoding and then learns to map an input query to the response representation, which is in turn decoded as a response sentence. Both quantitative and qualitative evaluations show that our model generates more fluent, relevant, and diverse responses than existing state-of-the-art methods.

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Kashif Khan, Gaurav Sahu, Vikash Balasubramanian, Lili Mou, Olga Vechtomova. 2020-11-03. Adversarial Learning on the Latent Space for Diverse Dialog Generation. https://arxiv.org/abs/1911.03817

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