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Interactively learning visually grounded word meanings from a human tutor

机译:互动地从导师那里学习基于视觉的单词含义

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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 effect of different dialogue policies and capabilities on 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 as well as incrementally constructed dialogue turns.
机译:我们提出了一种多模式对话系统,用于从人类导师那里交互式学习可感知的单词含义。该系统集成了一个增量式语义解析/生成框架-动态语法和带记录的类型理论(DS-TTR)-与一组视觉分类器,这些分类器在整个交互过程中均得到学习,并基于其产生的含义表示。我们将此系统与模拟的人类导师互动使用,以研究不同的对话策略和能力对所学含义,学习率以及导师付出的努力/成本的准确性的影响。我们表明,学习主体的整体绩效受到以下因素的影响:(1)主动进行对话的人; (2)表达/使用其对视觉属性的置信度的能力; (3)处理椭圆形以及逐步构造的对话转弯的能力。

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