Title
Optimising incremental dialogue decisions using information density for interactive systems
Abstract
Incremental processing allows system designers to address several discourse phenomena that have previously been somewhat neglected in interactive systems, such as backchannels or barge-ins, but that can enhance the responsiveness and naturalness of systems. Unfortunately, prior work has focused largely on deterministic incremental decision making, rendering system behaviour less flexible and adaptive than is desirable. We present a novel approach to incremental decision making that is based on Hierarchical Reinforcement Learning to achieve an interactive optimisation of Information Presentation (IP) strategies, allowing the system to generate and comprehend backchannels and barge-ins, by employing the recent psycholinguistic hypothesis of information density (ID) (Jaeger, 2010). Results in terms of average rewards and a human rating study show that our learnt strategy outperforms several baselines that are not sensitive to ID by more than 23%.
Year
Venue
Keywords
2012
EMNLP-CoNLL
comprehend backchannels,system behaviour,information presentation,deterministic incremental decision,incremental processing,interactive optimisation,interactive system,incremental decision making,hierarchical reinforcement learning,information density,incremental dialogue decision,system designer
Field
DocType
Volume
Information density,Computer science,Naturalness,Baseline (configuration management),Artificial intelligence,Rendering (computer graphics),Machine learning,Information presentation,Reinforcement learning
Conference
D12-1
Citations 
PageRank 
References 
22
0.80
22
Authors
4
Name
Order
Citations
PageRank
Nina Dethlefs121120.22
Helen Hastie2859.00
Verena Rieser342336.46
Oliver Lemon4107286.38