An Empirical B ayes Framework for Open-Domain Dialogue Generation (2023)

Abstract: 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.

## introduction an open-domain dialogue agent, or chatbot, aims to engage users in meaningful conversation by generating diverse, contextually coherent dialogue. in recent years, advances in deep learning and parallel computing have resulted in significant progress in open-domain dialogue research. architectures incorporating large transformer-based pretrained language models (plms) such as bert @xcite , t5 @xcite , and @xcite have achieved state of the art performance. however, despite recent progress, the fundamental issues of response diversity persists. ## bayesian open-domain dialogue via empirical bayes (bodeb) bodeb involves constructing a bayesian opendomain dialogue agent by leveraging pretrained langauge model parameters for prior definition and approximate posterior initialization. for this paper, we will utilize the gpt-2 and dialogpt plms. while both pretrained models are architecturally identical, they differ when it comes to pretraining. dialogpt has been pretrained exclusively for the task of multi-turn response generation, while gpt-2's pretraining is more general in scope. ## results & discussion the automatic evaluation and human evaluation results are presented in it is also apparent that responses generated by bodeb demonstrate greater contextual coherence relative to all other baselines. since both bodeb g and bodeb m attained higher ue-scores compared to other baselines on both corpora. furthermore, for human evaluation, bodeb m attained a large percentage of wins against dialogpt, ua-cvae, and cvae. bodeb m also generally achieves better results in terms of coherence relative to bodeb g . this is evidenced by the higher ue-scores attained by bodeb m when applied to gpt-2/dialogpt on both corpora. human evaluation also supports this observation as bodeb m achieves a high percentage of wins and a low percentage of losses when compared to bodeb g . this confirms our hypothesis in section 3.2. comparison with moped we directly apply moped to gpt-2 and dialogpt and summarized the evaluation results in ## conclusion in this paper, we introduced bodeb, an empirical bayes framework for creating a bayesian opendomain dialogue agent that can be directly applied to any plm. we empirically demonstrate that a bodeb-based bayesian dialogue agent is capable of producing more diverse and coherent responses compared to variational agents. as bnns allow for predictive uncertainty quantification, future research could involve exploring potential correlations between predictive uncertainty (comprising aleatoric and epistemic uncertainty) and various aspects of response quality, such as diversity and coherence. another avenue for future work could also involve exploring the efficacy of bodeb when applied to other language generation tasks.