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001 978-3-319-26200-0
003 DE-He213
005 20200421111207.0
007 cr nn 008mamaa
008 160208s2016 gw | s |||| 0|eng d
020 _a9783319262000
_9978-3-319-26200-0
024 7 _a10.1007/978-3-319-26200-0
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aChinaei, Hamidreza.
_eauthor.
245 1 0 _aBuilding Dialogue POMDPs from Expert Dialogues
_h[electronic resource] :
_bAn end-to-end approach /
_cby Hamidreza Chinaei, Brahim Chaib-draa.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aVII, 119 p. 22 illus., 21 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
505 0 _a1 Introduction -- 2 A few words on topic modeling -- 3 Sequential decision making in spoken dialog management -- 4 Learning the dialog POMDP model components -- 5 Learning the reward function -- 6 Application on healthcare dialog management -- 7 Conclusions and future work.
520 _aThis book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. It presents POMDP as a formal framework to represent uncertainty explicitly while supporting automated policy solving. The authors propose and implement an end-to-end learning approach for dialogue POMDP model components. Starting from scratch, they present the state, the transition model, the observation model and then finally the reward model from unannotated and noisy dialogues. These altogether form a significant set of contributions that can potentially inspire substantial further work. This concise manuscript is written in a simple language, full of illustrative examples, figures, and tables. Provides insights on building dialogue systems to be applied in real domain Illustrates learning dialogue POMDP model components from unannotated dialogues in a concise format Introduces an end-to-end approach that makes use of unannotated and noisy dialogue for learning each component of dialogue POMDPs.
650 0 _aEngineering.
650 0 _aUser interfaces (Computer systems).
650 0 _aArtificial intelligence.
650 0 _aComputational linguistics.
650 0 _aElectrical engineering.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aUser Interfaces and Human Computer Interaction.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Linguistics.
700 1 _aChaib-draa, Brahim.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319261980
830 0 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-26200-0
912 _aZDB-2-ENG
942 _cEBK
999 _c54232
_d54232