Latent Variable Dialogue Models and their Diversity
arXiv:1702.05962
Abstract
We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the `boring output' issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.
Accepted at EACL 2017
References in corpus (5)
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- Neural Responding Machine for Short-Text Conversation
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Cited by in corpus (16)
- Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
- A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions
- Latent Intention Dialogue Models
- Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
- Another Diversity-Promoting Objective Function for Neural Dialogue Generation
- Variational Attention for Sequence-to-Sequence Models
- Decoupling Strategy and Generation in Negotiation Dialogues
- Adaptive Parameterization for Neural Dialogue Generation
- Natural Language Generation with Neural Variational Models
- On the Integration of LinguisticFeatures into Statistical and Neural Machine Translation
- Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity
- Latent Topic Conversational Models
- Modelling Latent Skills for Multitask Language Generation
- Affective Neural Response Generation
- Question Generation from SQL Queries Improves Neural Semantic Parsing
- Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network