CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation

By Haolan Zhan, Hongshen Chen, Hainan Zhang. 2021

27 citations recorded by the source corpus.

Knowledge-grounded dialogue generation has achieved promising performance with the engagement of external knowledge sources. Typical approaches towards this task usually perform relatively independent two sub-tasks, i.e., knowledge selection and knowledge-aware response generation. In this paper, in order to improve the diversity of both knowledge selection and knowledge-aware response generation, we propose a collaborative latent variable (CoLV) model to integrate these two aspects simultaneously in separate yet collaborative latent spaces, so as to capture the inherent correlation between knowledge selection and response generation. During generation, our proposed model firstly draws knowledge candidate from the latent space conditioned on the dialogue context, and then samples a response from another collaborative latent space conditioned on both the context and the selected knowledge. Experimental results on two widely-used knowledge-grounded dialogue datasets show that our model outperforms previous methods on both knowledge selection and response generation.

Subfields: Artificial Intelligence, Retrieval-Augmented Generation, Natural Language Processing

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Citations in the corpus

Hindsight: Posterior-guided training of retrievers for improved open-ended generation (2021, methodology): “Prior work has also modeled the posterior of various probabilistic models (Lian et al., 2019; Kim et al., 2020; Zhan et al., 2021) or used reinforcement learning (Zhao et al.”