Learning and Evaluation of Dialogue Strategies for New Applications: Empirical Methods for Optimization from Small Data Sets | Citedby Results | Computational Linguistics | MIT Press

Skip to Main Content

Skip Nav Destination

Learning and Evaluation of Dialogue Strategies for New Applications: Empirical Methods for Optimization from Small Data Sets

Computational Linguistics (2011) 37 (1): 153–196.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

Conversational AI for multi-agent communication in Natural Language

Oliver Lemon
Stefano V. Albrecht
Michael Woolridge

AI Communications (2022) 35 (4): 295.

Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching

Ola G. El‐Taliawi
Nihit Goyal
Michael Howlett

Review of Policy Research (2021) 38 (6): 640.

Reward estimation for dialogue policy optimisation

Pei-Hao Su
Milica Gašić
Steve Young

Computer Speech & Language (2018) 51: 24.

Reinforcement-learning based dialogue system for human–robot interactions with socially-inspired rewards

Emmanuel Ferreira
Fabrice Lefèvre

Computer Speech & Language (2015) 34 (1): 256.

Machine Learning for Social Multiparty Human--Robot Interaction

Simon Keizer
Mary Ellen Foster
Zhuoran Wang
Oliver Lemon

ACM Transactions on Interactive Intelligent Systems (2014) 4 (3): 1.

Natural Language Generation as Incremental Planning Under Uncertainty: Adaptive Information Presentation for Statistical Dialogue Systems

Verena Rieser
Oliver Lemon
Simon Keizer

IEEE/ACM Transactions on Audio, Speech, and Language Processing (2014) 22 (5): 979.

Adaptive Generation in Dialogue Systems Using Dynamic User Modeling

Srinivasan Janarthanam
Oliver Lemon

Computational Linguistics (2014) 40 (4): 883.

Introduction to special issue on machine learning for adaptivity in spoken dialogue systems

Oliver Lemon
Olivier Pietquin

ACM Transactions on Speech and Language Processing (2011) 7 (3): 1.