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%.
An approach based on Hierarchical Reinforcement Learning is presented in REF ).
Optimising Incremental Dialogue Decisions Using Information Density for Interactive Systems
EMNLP
Computer Science