A Persona-Based Multi-turn Conversation Model in an Adversarial Learning Framework

By Oluwatobi Olabiyi, Anish Khazane, Erik T. Mueller. 2018

10 citations recorded by the source corpus.

In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to multi-turn dialogue by modifying the state-of-the-art hredGAN architecture. To achieve this, we introduce an additional input modality into the encoder and decoder of hredGAN to capture other attributes such as speaker identity, location, sub-topics, and other external attributes that might be available from the corpus of human-to-human interactions. The resulting persona hredGAN (phredGAN) shows better performance than both the existing persona-based Seq2Seq and hredGAN models when those external attributes are available in a multi-turn dialogue corpus. This superiority is demonstrated on TV drama series with character consistency (such as Big Bang Theory and Friends) and customer service interaction datasets such as Ubuntu dialogue corpus in terms of perplexity, BLEU, ROUGE, and Distinct n-gram scores.

Subfields: Artificial Intelligence, Natural Language Processing, Generative Models

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Citations in the corpus

Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “Recent work in dialogue incorporates personas, or personality descriptions that ground speaker’s chat, such as I love fishing (Zhang et al., 2018; Shuster et al., 2018; Mazaré et al., 2018; Olabiyi et al., 2018; Li et al., 2016).”
A survey on empathetic dialogue systems (2020, background): “[92] integrated speaker attributes with a hierarchical recurrent encoder-decoder network (HRED) [107].”