Generative commonsense knowledge subgraph retrieval for open-domain dialogue response generation.
Journal:
Neural networks : the official journal of the International Neural Network Society
PMID:
39216293
Abstract
Grounding on a commonsense knowledge subgraph can help the model generate more informative and diverse dialogue responses. Prior Traverse-based works explicitly retrieve a subgraph from the external knowledge base (eKB). Notably, the available knowledge is strictly restricted by the eKB. To break this restriction, Generative Retrieval methods externalize knowledge from the language model. However, they always generate boring knowledge due to their one-pass externalization procedure. This work proposes a novel TiLM Traverse in Language Model (TiLM), which uses three 'Chain-of-Thought' sub-tasks, i.e., Query Entity Production, Topic Entity Prediction, and Knowledge Subgraph Completion, to build a high-quality knowledge subgraph to ground the next Response Generation without explicitly accessing the eKB in inference. Experimental results on both Chinese and English datasets demonstrate TiLM's outstanding performance even only with a small scale of parameters.