KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings

Rajdeep Sarkar, Mihael Arcan, John McCrae


Abstract
Knowledge-grounded dialogue systems utilise external knowledge such as knowledge graphs to generate informative and appropriate responses. A crucial challenge of such systems is to select facts from a knowledge graph pertinent to the dialogue context for response generation. This fact selection can be formulated as path traversal over a knowledge graph conditioned on the dialogue context. Such paths can originate from facts mentioned in the dialogue history and terminate at the facts to be mentioned in the response. These walks, in turn, provide an explanation of the flow of the conversation. This work proposes KG-CRuSE, a simple, yet effective LSTM based decoder that utilises the semantic information in the dialogue history and the knowledge graph elements to generate such paths for effective conversation explanation. Extensive evaluations showed that our model outperforms the state-of-the-art models on the OpenDialKG dataset on multiple metrics.
Anthology ID:
2022.nlp4convai-1.9
Volume:
Proceedings of the 4th Workshop on NLP for Conversational AI
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Bing Liu, Alexandros Papangelis, Stefan Ultes, Abhinav Rastogi, Yun-Nung Chen, Georgios Spithourakis, Elnaz Nouri, Weiyan Shi
Venue:
NLP4ConvAI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
98–107
Language:
URL:
https://aclanthology.org/2022.nlp4convai-1.9
DOI:
10.18653/v1/2022.nlp4convai-1.9
Bibkey:
Cite (ACL):
Rajdeep Sarkar, Mihael Arcan, and John McCrae. 2022. KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings. In Proceedings of the 4th Workshop on NLP for Conversational AI, pages 98–107, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings (Sarkar et al., NLP4ConvAI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.nlp4convai-1.9.pdf
Video:
 https://aclanthology.org/2022.nlp4convai-1.9.mp4
Code
 rajbsk/kg-cruse
Data
OpenDialKG