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arXiv · 1709.10426

Training an adaptive dialogue policy for interactive learning of visually grounded word meanings

Yanchao Yu·Arash Eshghi·Oliver Lemon
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Abstract

We present a multi-modal dialogue system for interactive learning of perceptually grounded word meanings from a human tutor. The system integrates an incremental, semantic parsing/generation framework - Dynamic Syntax and Type Theory with Records (DS-TTR) - with a set of visual classifiers that are learned throughout the interaction and which ground the meaning representations that it produces. We use this system in interaction with a simulated human tutor to study the effects of different dialogue policies and capabilities on the accuracy of learned meanings, learning rates, and efforts/costs to the tutor. We show that the overall performance of the learning agent is affected by (1) who takes initiative in the dialogues; (2) the ability to express/use their confidence level about visual attributes; and (3) the ability to process elliptical and incrementally constructed dialogue turns. Ultimately, we train an adaptive dialogue policy which optimises the trade-off between classifier accuracy and tutoring costs.

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Yanchao Yu, Arash Eshghi, Oliver Lemon. 2017-09-29. Training an adaptive dialogue policy for interactive learning of visually grounded word meanings. https://doi.org/10.18653/v1%2Fw16-3643

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