Name
Title | ||
|---|---|---|
Optimising incremental generation for spoken dialogue systems: reducing the need for fillers |
Abstract | ||
|---|---|---|
Recent studies have shown that incremental systems are perceived as more reactive, natural, and easier to use than non-incremental systems. However, previous work on incremental NLG has not employed recent advances in statistical optimisation using machine learning. This paper combines the two approaches, showing how the update, revoke and purge operations typically used in incremental approaches can be implemented as state transitions in a Markov Decision Process. We design a model of incremental NLG that generates output based on micro-turn interpretations of the user's utterances and is able to optimise its decisions using statistical machine learning. We present a proof-of-concept study in the domain of Information Presentation (IP), where a learning agent faces the trade-off of whether to present information as soon as it is available (for high reactiveness) or else to wait until input ASR hypotheses are more reliable. Results show that the agent learns to avoid long waiting times, fillers and self-corrections, by re-ordering content based on its confidence. |
Year | Venue | Keywords |
|---|---|---|
2012 | INLG | markov decision process,statistical optimisation,incremental nlg,recent advance,information presentation,machine learning,incremental system,dialogue system,recent study,incremental approach,incremental generation,statistical machine learning |
Field | DocType | Citations |
Learning agent,Computer science,Markov decision process,Artificial intelligence,Machine learning,Information presentation | Conference | 8 |
PageRank | References | Authors |
0.63 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nina Dethlefs | 1 | 211 | 20.22 |
| Helen Hastie | 2 | 85 | 9.00 |
| Verena Rieser | 3 | 423 | 36.46 |
| Oliver Lemon | 4 | 1072 | 86.38 |