OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
Open accessAbstract
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.
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- Authors
- Moon, S., Shah, P., Kumar, A., & Subba, R.
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 845-854
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2950457956
- Cited by
- 306
- References
- 1
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 26 · 2023: 60 · 2022: 66 · 2021: 62 · 2020: 53 · 2019: 4
- FWCI
- 17.69
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- knowledge graphs, explainable reasoning, entity retrieval
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