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Found 3 papers, 2 papers with code
Neural Conversation Models and How to Rein Them in: A Survey of Failures and Fixes
In this survey, we interpret Grice's maxims of cooperative conversation from the perspective of this specific research area and systematize the literature under the aspect of what makes a contribution appropriate: A neural conversation model has to be fluent, informative, consistent, coherent, and follow social norms.
Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer
To improve the coherence and knowledge retrieval capabilities of non-task-oriented dialogue systems, recent Transformer-based models aim to integrate fixed background context.
A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models
Fully data driven Chatbots for non-goal oriented dialogues are known to suffer from inconsistent behaviour across their turns, stemming from a general difficulty in controlling parameters like their assumed background personality and knowledge of facts.
