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How should dialogue systems represent uncertainty from noisy speech input?
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Questions in this line of inquiry 10
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- How should dialogue systems represent and update uncertainty from noisy ASR input?
- How do probabilistic dialogue systems handle ASR errors differently?
- How do belief distributions help systems recover from speech recognition errors?
- Can dialogue systems abstain from responding when uncertainty is too high?
- Does the same uncertainty-driven logic appear in other conversation systems?
- Can offline RL and pragmatic inference together improve dialogue agent reliability?
- How does structured self-dialogue improve uncertainty assessment over confidence scores?
- Can dialogue agents be reliable but still feel inflexible or cold?
- Can systems guide users adaptively without imposing predetermined dialogue structures?
- What moves become possible when you represent ASR as a noisy observation model?