Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems.

P. Su, D. Vandyke, M. Gasic, N. Mrksic, T. Wen, and S. Young. SIGDIAL Conference, page 417-421. The Association for Computer Linguistics, (2015)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Nikola Pantscheff

Nikola Sirakoff

Nikola Brzovic

Nikola Raikoff

Nikola Dimitrov

Other publications of authors with the same name

Policy committee for adaptation in multi-domain spoken dialogue systems.M. Gasic, N. Mrksic, P. hao Su, D. Vandyke, T. Wen, and S. Young. ASRU, page 806-812. IEEE, (2015)On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems.P. Su, M. Gasic, N. Mrksic, L. Rojas-Barahona, S. Ultes, D. Vandyke, T. Wen, and S. Young. ACL (1), The Association for Computer Linguistics, (2016)ConvFiT: Conversational Fine-Tuning of Pretrained Language Models.I. Vulic, P. Su, S. Coope, D. Gerz, P. Budzianowski, I. Casanueva, N. Mrksic, and T. Wen. EMNLP (1), page 1151-1168. Association for Computational Linguistics, (2021)Dialogue manager domain adaptation using Gaussian process reinforcement learning.M. Gasic, N. Mrksic, L. Rojas-Barahona, P. Su, S. Ultes, D. Vandyke, T. Wen, and S. Young. CoRR, (2016)Training Neural Response Selection for Task-Oriented Dialogue Systems.M. Henderson, I. Vulic, D. Gerz, I. Casanueva, P. Budzianowski, S. Coope, G. Spithourakis, T. Wen, N. Mrksic, and P. Su. ACL (1), page 5392-5404. Association for Computational Linguistics, (2019)A Network-based End-to-End Trainable Task-oriented Dialogue System.T. Wen, D. Vandyke, N. Mrksic, M. Gasic, L. Rojas-Barahona, P. Su, S. Ultes, and S. Young. EACL (1), page 438-449. Association for Computational Linguistics, (2017)Conditional Generation and Snapshot Learning in Neural Dialogue Systems.T. Wen, M. Gasic, N. Mrksic, L. Rojas-Barahona, P. Su, S. Ultes, D. Vandyke, and S. Young. EMNLP, page 2153-2162. The Association for Computational Linguistics, (2016)ConveRT: Efficient and Accurate Conversational Representations from Transformers.M. Henderson, I. Casanueva, N. Mrksic, P. Su, T. Wen, and I. Vulic. CoRR, (2019)Neural Belief Tracker: Data-Driven Dialogue State Tracking.N. Mrksic, D. Séaghdha, T. Wen, B. Thomson, and S. Young. CoRR, (2016)A Repository of Conversational Datasets.M. Henderson, P. Budzianowski, I. Casanueva, S. Coope, D. Gerz, G. Kumar, N. Mrksic, G. Spithourakis, P. Su, I. Vulic and 1 other author(s). CoRR, (2019)

BibSonomy

Disambiguation of "Mrksic, Nikola"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems.

Please choose a person to relate this publication to

Nikola Pantscheff

Nikola Sirakoff

Nikola Brzovic

Nikola Raikoff

Nikola Dimitrov

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Mrksic, Nikola"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems.

Please choose a person to relate this publication to

Nikola Pantscheff

Nikola Sirakoff

Nikola Brzovic

Nikola Raikoff

Nikola Dimitrov

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems.