Summary of the paper
| Title | Evaluation of Online Dialogue Policy Learning Techniques |
|---|---|
| Authors | Alexandros Papangelis, Vangelis Karkaletsis and Fillia Makedon |
| Abstract | The number of applied Dialogue Systems is ever increasing in several service providing and other applications as a way to efficiently and inexpensively serve large numbers of customers. A DS that employs some form of adaptation to the environment and its users is called an Adaptive Dialogue System (ADS). A significant part of the research community has lately focused on ADS and many existing or novel techniques are being applied to this problem. One of the most promising techniques is Reinforcement Learning (RL) and especially online RL. This paper focuses on online RL techniques used to achieve adaptation in Dialogue Management and provides an evaluation of various such methods in an effort to aid the designers of ADS in deciding which method to use. To the best of our knowledge there is no other work to compare online RL techniques on the dialogue management problem. |
| Topics | Dialogue, Statistical and machine learning methods |
Full paper  |
Evaluation of Online Dialogue Policy Learning Techniques |
| Bibtex | @InProceedings{PAPANGELIS12.291, author = {Alexandros Papangelis and Vangelis Karkaletsis and Fillia Makedon}, title = {Evaluation of Online Dialogue Policy Learning Techniques}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |