An Empirical Bayes Framework for Open-Domain Dialogue Generation
Jing Yang Lee Kong Aik Lee Woon-Seng Gan
To engage human users in meaningful conversation, open-domain dialogue agents
are required to generate diverse and contextually coherent dialogue. Despite
recent advancements, which can be attributed to the usage of pretrained
language models, the generation of diverse and coherent dialogue remains an
open research problem. A popular approach to address this issue involves the
adaptation of variational frameworks. However, while these approaches
successfully improve diversity, they tend to compromise on contextual
coherence. Hence, we propose the Bayesian Open-domain Dialogue with Empirical
Bayes (BODEB) framework, an empirical bayes framework for constructing an
Bayesian open-domain dialogue agent by leveraging pretrained parameters to
inform the prior and posterior parameter distributions. Empirical results show
that BODEB achieves better results in terms of both diversity and coherence
compared to variational frameworks.