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• W2950457956 abstract "We study a conversational reasoning model that strategically traverses through a large-scale 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 role-playing 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-of-the-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." @default.
• W2950457956 created "2019-06-27" @default.
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• W2950457956 date "2019-01-01" @default.
• W2950457956 modified "2023-10-14" @default.
• W2950457956 title "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs" @default.
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• W2950457956 doi "https://doi.org/10.18653/v1/p19-1081" @default.
• W2950457956 hasPublicationYear "2019" @default.
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