@inproceedings{sarkar-etal-2022-kg,
title = "{KG}-{CR}u{SE}: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings",
author = "Sarkar, Rajdeep and
Arcan, Mihael and
McCrae, John",
editor = "Liu, Bing and
Papangelis, Alexandros and
Ultes, Stefan and
Rastogi, Abhinav and
Chen, Yun-Nung and
Spithourakis, Georgios and
Nouri, Elnaz and
Shi, Weiyan",
booktitle = "Proceedings of the 4th Workshop on NLP for Conversational AI",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nlp4convai-1.9",
doi = "10.18653/v1/2022.nlp4convai-1.9",
pages = "98--107",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings
%A Sarkar, Rajdeep
%A Arcan, Mihael
%A McCrae, John
%Y Liu, Bing
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%Y Spithourakis, Georgios
%Y Nouri, Elnaz
%Y Shi, Weiyan
%S Proceedings of the 4th Workshop on NLP for Conversational AI
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F sarkar-etal-2022-kg
%X 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.
%R 10.18653/v1/2022.nlp4convai-1.9
%U https://aclanthology.org/2022.nlp4convai-1.9
%U https://doi.org/10.18653/v1/2022.nlp4convai-1.9
%P 98-107
Markdown (Informal)
[KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings](https://aclanthology.org/2022.nlp4convai-1.9) (Sarkar et al., NLP4ConvAI 2022)
ACL