OpenDialKG: Explainable Conversational Reasoning with
Attention-based Walks over Knowledge Graphs
Abstract
We study a conversational reasoning model
that strategically traverses through a largescale common fact knowledge graph (KG) to
introduce engaging and contextually diverse
entities and attributes. For this study, we collect a new Open-ended Dialog ? KG parallel corpus called OpenDialKG, where each
utterance from 15K human-to-human roleplaying dialogs is manually annotated with
ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+
facts. We then propose the DialKG Walker
model that learns the symbolic transitions of
dialog contexts as structured traversals over
KG, and predicts natural entities to introduce
given previous dialog contexts via a novel
domain-agnostic, attention-based graph path
decoder. Automatic and human evaluations
show that our model can retrieve more natural and human-like responses than the state-ofthe-art baselines or rule-based models, in both
in-domain and cross-domain tasks. The proposed model also generates a KG walk path
for each entity retrieved, providing a natural
way to explain conversational reasoning