Semantic-aware conditional variational autoencoder for one-to-many dialogue generation

Wang, Ye; Liao, Jingbo; Yu, Hong; Leng, Jiaxu

doi:10.1007/s00521-022-07182-9

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Gao et al [17] introduced a discrete variable with explicit semantics in CVAE, using the semantic distance between latent variables to maintain good diversity among the sampled latent variables. Wang et al [18] proposed the semantic-aware conditional variant autoencoder (S-CVAE) model. S-CVAE can generate diverse conversational text by utilizing embedded classifiers and feature decoupling modules.…”

Section: Related Workmentioning

confidence: 99%

Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation

Wang

Liao

et al. 2022

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Currently end-to-end deep learning based open-domain dialogue systems remain black box models, making it easy to generate irrelevant contents with data-driven models. Specifically, latent variables are highly entangled with different semantics in the latent space due to the lack of priori knowledge to guide the training. To address this problem, this paper proposes to harness the generative model with a priori knowledge through a cognitive approach involving mesoscopic scale feature disentanglement. Particularly, the model integrates the macro-level guided-category knowledge and micro-level open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables within the mesoscopic scale. Besides, we propose a new metric for open-domain dialogues, which can objectively evaluate the interpretability of the latent space distribution. Finally, we validate our model on different datasets and experimentally demonstrate that our model is able to generate higher quality and more interpretable dialogues than other models.

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Section: Related Workmentioning

confidence: 99%