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        <title>CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations</title>
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    <abstract>Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation.</abstract>
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%0 Conference Proceedings
%T CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations
%A Xu, Lin
%A Zhou, Qixian
%A Fu, Jinlan
%A Kan, Min-Yen
%A Ng, See-Kiong
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F xu-etal-2022-corefdiffs
%X Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation.
%U https://aclanthology.org/2022.coling-1.38
%P 471-484

Markdown (Informal)

[CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations](https://aclanthology.org/2022.coling-1.38) (Xu et al., COLING 2022)

• CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (Xu et al., COLING 2022)

ACL

• Lin Xu, Qixian Zhou, Jinlan Fu, Min-Yen Kan, and See-Kiong Ng. 2022. CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 471–484, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.