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

Constructing responses in task-oriented dialogue systems typically relies on information sources such the current dialogue state or external databases. This paper presents a novel approach to knowledge-grounded response generation
that combines retrieval-augmented language models with logical reasoning. The approach revolves around a knowledge graph representing the current dialogue state and background
information, and proceeds in three steps.
The knowledge graph is first enriched with logically
derived facts inferred using probabilistic logical programming. A neural model is then employed at each turn to score the conversational relevance of each node and edge
of this extended graph. Finally, the elements with highest relevance scores are converted to a natural language form, and are integrated into the prompt for the neural conversational model employed to generate the system response.
We investigate the benefits of the proposed approach
on two datasets (KVRET and Graph-WOZ) along with a human evaluation. Experimental results show that the combination of
(probabilistic) logical reasoning with conversational
relevance scoring does increase both the factuality and fluency of the responses.