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Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational AutoEncoders
Self-separated Conditional Variational AutoEncoder (abbreviated as SepaCVAE) is proposed that introduces group information to regularize the latent variables, which enhances CVAE by improving the responses’ relevance and coherence while maintaining their diversity and informativeness.
Regularizing Dialogue Generation by Imitating Implicit Scenarios
- Shaoxiong Feng, Xuancheng Ren, Hongshen Chen, Bin Sun, Kan Li, Xu Sun
- Computer ScienceEMNLP
- 5 October 2020
This work proposes to improve generative dialogue systems from the scenario perspective, where both dialogue history and future conversation are taken into account to implicitly reconstruct the scenario knowledge.
Neural Dialogue Generation Methods in Open Domain: A Survey
THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses
Diversifying Neural Dialogue Generation via Negative Distillation
This paper proposes a novel negative training paradigm, called negative distillation, to keep the model away from the undesirable generic responses while avoiding the above problems, and shows that this method outperforms previous negative training methods significantly.
Neural Dialogue Generation Methods in Open Domain: A Survey empirical
This survey elaborated the research history of these existing generative methods, and roughly divided them into six categories, i.e., Encoder-Decoder framework-based methods, Hierarchical Recurrent EncodedDecoder (HRED) based methods, Variational Autoencoder (VAE)-basedmethods, Reinforcement Learning (RL)based methods, GenerativeAdversarial Network (GAN)-based Methods, and pretraining-model-based Methods.
Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning
A multi-view attribute-enhanced dialogue learning framework that strengthens the attribute-related features more robustly and comprehensively and can improve the performance of models by enhancing dialogue attributes and fusing view-speciﬁc knowledge.