CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Dialogue GenerationDiversityResponse GenerationSimilar Papers 제목 키워드 기반
CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization
Due to the potential for exploratory reasoning of Latent Visual Reasoning, recent works tend to enable MLLMs (Multimodal Large Language Models) to perform visual reasoning by propagating continuous hidden states instead …
Reinforcement LearningVisual ReasoningRevColV2: Exploring Disentangled Representations in Masked Image Modeling
Masked image modeling (MIM) has become a prevalent pre-training setup for vision foundation models and attains promising performance. Despite its success, existing MIM methods discard the decoder network during downstrea…
Decoderimage-classificationImage Classificationobject-detection+3A Hybrid Latent Variable Neural Network Model for Item Recommendation
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filtering suffers from the cold-start problem…
Collaborative FilteringBridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation
Knowledge selection plays an important role in knowledge-grounded dialogue, which is a challenging task to generate more informative responses by leveraging external knowledge. Recently, latent variable models have been …
DecoderDialogue GenerationKnowledge DistillationSyntactically Diverse Adversarial Network for Knowledge-Grounded Conversation Generation
Generative conversation systems tend to produce meaningless and generic responses, which significantly reduce the user experience. In order to generate informative and diverse responses, recent studies proposed to fuse k…
DiversityInformativeness