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arXiv · 1910.07931

PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable

Siqi Bao·Huang He·Fan Wang·Hua Wu·Haifeng Wang
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Abstract

Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. In this framework, we adopt flexible attention mechanisms to fully leverage the bi-directional context and the uni-directional characteristic of language generation. We also introduce discrete latent variables to tackle the inherent one-to-many mapping problem in response generation. Two reciprocal tasks of response generation and latent act recognition are designed and carried out simultaneously within a shared network. Comprehensive experiments on three publicly available datasets verify the effectiveness and superiority of the proposed framework.

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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang. 2019-10-17. PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable. https://arxiv.org/abs/1910.07931

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