Papers with end-to-end dialogue system

2 papers
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems (2022.findings-acl)

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Challenge: Existing systems that require extensive labor to process user requests are limited in their reasoning capabilities and require extensive manual effort to design.
Approach: They propose a method that allows a transformer model to walk on a large-scale knowledge graph to generate responses by reasoning over differentiable knowledge graphs.
Outcome: The proposed method allows a transformer model to walk on a large-scale knowledge graph to generate responses.
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue (D19-1)

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Challenge: Traditional recommendation systems produce static rather than interactive recommendations invariant to a user’s specific requests, clarifications, or current mood.
Approach: They use a goal-driven recommendation dialogue dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend.
Outcome: The proposed system can converse and recommend movies to humans without considering the task goal itself.

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