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        <title>Memory Grounded Conversational Reasoning</title>
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        <namePart type="given">Seungwhan</namePart>
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            <namePart type="given">Sebastian</namePart>
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            <namePart type="given">Ruihong</namePart>
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    <abstract>We demonstrate a conversational system which engages the user through a multi-modal, multi-turn dialog over the user’s memories. The system can perform QA over memories by responding to user queries to recall specific attributes and associated media (e.g. photos) of past episodic memories. The system can also make proactive suggestions to surface related events or facts from past memories to make conversations more engaging and natural. To implement such a system, we collect a new corpus of memory grounded conversations, which comprises human-to-human role-playing dialogs given synthetic memory graphs with simulated attributes. Our proof-of-concept system operates on these synthetic memory graphs, however it can be trained and applied to real-world user memory data (e.g. photo albums, etc.) We present the architecture of the proposed conversational system, and example queries that the system supports.</abstract>
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%0 Conference Proceedings
%T Memory Grounded Conversational Reasoning
%A Moon, Seungwhan
%A Shah, Pararth
%A Subba, Rajen
%A Kumar, Anuj
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F moon-etal-2019-memory-grounded
%X We demonstrate a conversational system which engages the user through a multi-modal, multi-turn dialog over the user’s memories. The system can perform QA over memories by responding to user queries to recall specific attributes and associated media (e.g. photos) of past episodic memories. The system can also make proactive suggestions to surface related events or facts from past memories to make conversations more engaging and natural. To implement such a system, we collect a new corpus of memory grounded conversations, which comprises human-to-human role-playing dialogs given synthetic memory graphs with simulated attributes. Our proof-of-concept system operates on these synthetic memory graphs, however it can be trained and applied to real-world user memory data (e.g. photo albums, etc.) We present the architecture of the proposed conversational system, and example queries that the system supports.
%R 10.18653/v1/D19-3025
%U https://aclanthology.org/D19-3025
%U https://doi.org/10.18653/v1/D19-3025
%P 145-150

Markdown (Informal)

[Memory Grounded Conversational Reasoning](https://aclanthology.org/D19-3025) (Moon et al., EMNLP-IJCNLP 2019)

• Memory Grounded Conversational Reasoning (Moon et al., EMNLP-IJCNLP 2019)

ACL

• Seungwhan Moon, Pararth Shah, Rajen Subba, and Anuj Kumar. 2019. Memory Grounded Conversational Reasoning. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 145–150, Hong Kong, China. Association for Computational Linguistics.