Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88898
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Shen, XY | - |
dc.creator | Su, H | - |
dc.creator | Li, YR | - |
dc.creator | Li, WJ | - |
dc.creator | Niu, SZ | - |
dc.creator | Zhao, Y | - |
dc.creator | Aizawa, A | - |
dc.creator | Long, GP | - |
dc.date.accessioned | 2021-01-11T02:42:19Z | - |
dc.date.available | 2021-01-11T02:42:19Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/88898 | - |
dc.language.iso | en | en_US |
dc.publisher | Association for Computational Linguistics | en_US |
dc.rights | ©2017 Association for Computational Linguistics | en_US |
dc.rights | Creative Commons 4.0 BY (Attribution) license (https://creativecommons.org/licenses/by/4.0/) | en_US |
dc.rights | The following publication Shen, X. Y., Su, H., Li, Y. R., Li, W. J., Niu, S. Z., Zhao, Y., . . . Long, G. P. (2017). A conditional variational framework for dialog generation. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, July 2017, Vancouver, Canada, v. 2, p. 504-509, 504-509 is available at https://dx.doi.org/10.18653/v1/P17-2080 | en_US |
dc.title | A conditional variational framework for dialog generation | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 504 | - |
dc.identifier.epage | 509 | - |
dc.identifier.doi | 10.18653/v1/P17-2080 | - |
dcterms.abstract | Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, July 2017, Vancouver, Canada, v. 2, p. 504-509 | - |
dcterms.issued | 2017 | - |
dc.identifier.isi | WOS:000493992300080 | - |
dc.relation.conference | Association-for-Computational-Linguistics. Annual Meeting [ACL. Annual Meeting] | - |
dc.description.validate | 202101 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Shen_Conditional_Variational_Framework.pdf | 236.8 kB | Adobe PDF | View/Open |
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