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Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
A novel encoder-decoder based generative adversarial learning framework, Posterior Generative Adversarial Network (Posterior-GAN), which consists of a forward and a backward generative discriminator to cooperatively encourage the generated response to be informative and coherent by two complementary assessment perspectives is proposed.
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.
Collaborative Group Learning
- Shaoxiong Feng, Hongshen Chen, Xuancheng Ren, Zhuoye Ding, Kan Li, Xu Sun
- Computer ScienceAAAI
- 16 September 2020
Empirical evaluations indicate that the proposed Collaborative Group Learning method significantly outperforms various state-of-the-art collaborative approaches whilst enhancing computational efficiency.
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
A novel training framework that extends the unidirectionaldistillation to the bidirectional distillation that encourages the student and its student peers to co-evolve by exchanging complementary knowledge with each other and improves the model generalization without sacrificing training efficiency.
Hierarchical Inductive Transfer for Continual Dialogue Learning
A hierarchical inductive transfer framework is proposed that enables new tasks to use general knowledge in the base adapter without being misled by diverse knowledge in task-specific adapters and obtains comparable performance under deployment-friendly model capacity.
Continue or SHIFT: Learning Conversational Patterns for Dialogue Generation
The loose coupling approach (LCA) is proposed to directly address the learning of such conversational patterns, which includes two parts: a high-level information selecting mechanism that enables the model to ignore the context or the previous part of the response, and a sampling-based response guide mechanism that mitigates the exposure bias problem in training.
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.
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.