Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152721
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dc.contributor.authorZhong, Peixiangen_US
dc.contributor.authorLiu, Yongen_US
dc.contributor.authorWang, Haoen_US
dc.contributor.authorMiao, Chunyanen_US
dc.date.accessioned2021-09-29T03:26:49Z-
dc.date.available2021-09-29T03:26:49Z-
dc.date.issued2021-
dc.identifier.citationZhong, P., Liu, Y., Wang, H. & Miao, C. (2021). Keyword-guided neural conversational model. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 35, 14568-14576.en_US
dc.identifier.issn2159-5399-
dc.identifier.issn978-1-57735-866-4-
dc.identifier.otherhttps://ojs.aaai.org/index.php/AAAI/issue/archive-
dc.identifier.urihttps://hdl.handle.net/10356/152721-
dc.description.abstractWe study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.en_US
dc.description.sponsorshipAI Singaporeen_US
dc.description.sponsorshipMinistry of Health (MOH)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationAlibaba-NTU-AIR2019B1en_US
dc.relationAISG-GC-2019-003en_US
dc.relationNRF-NRFI05- 2019-0002en_US
dc.relationMOH/NIC/COG04/2017en_US
dc.relationMOH/NIC/HAIG03/2017en_US
dc.rights© 2021 Association for the Advancement of Artificial Intelligence. All Rights Reserved. This paper was published in Proceedings of Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) and is made available with permission of Association for the Advancement of Artificial Intelligence.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleKeyword-guided neural conversational modelen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conferenceThirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)en_US
dc.contributor.researchAlibaba-NTU Singapore Joint Research Instituteen_US
dc.contributor.researchJoint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)en_US
dc.description.versionAccepted versionen_US
dc.identifier.volume35en_US
dc.identifier.spage14568en_US
dc.identifier.epage14576en_US
dc.subject.keywordsConversational Agenten_US
dc.subject.keywordsKeyword Transitionen_US
dc.citation.conferencelocationVirtual Conferenceen_US
dc.description.acknowledgementThis research is supported, in part, by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI) (Alibaba-NTU-AIR2019B1), Nanyang Technological Uni- versity, Singapore. This research is also supported, in part, by the National Research Foundation, Prime Minister’s Of- fice, Singapore under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003) and under its NRF Inves- tigatorship Programme (NRFI Award No. NRF-NRFI05- 2019-0002). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of National Re- search Foundation, Singapore. This research is also sup- ported, in part, by the Singapore Ministry of Health un- der its National Innovation Challenge on Active and Confi- dent Ageing (NIC Project No. MOH/NIC/COG04/2017 and MOH/NIC/HAIG03/2017).en_US
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