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Constructing responses in task-oriented dialogue systems typically relies oninformation sources such the current dialogue state or external databases. Thispaper presents a novel approach to knowledge-grounded response generation thatcombines retrieval-augmented language models with logical reasoning. Theapproach revolves around a knowledge graph representing the current dialoguestate and background information, and proceeds in three steps. The knowledgegraph is first enriched with logically derived facts inferred usingprobabilistic logical programming. A neural model is then employed at each turnto score the conversational relevance of each node and edge of this extendedgraph. Finally, the elements with highest relevance scores are converted to anatural language form, and are integrated into the prompt for the neuralconversational model employed to generate the system response.  We investigate the benefits of the proposed approach on two datasets (KVRETand GraphWOZ) along with a human evaluation. Experimental results show that thecombination of (probabilistic) logical reasoning with conversational relevancescoring does increase both the factuality and fluency of the responses.

Retrieval-Augmented Neural Response Generation Using Logical Reasoning and Relevance Scoring