面向视觉对话问题生成的提问者脑中大规模回答者
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
摘要：
提问者思维中的应答者（AQM）是最近提出的一个面向任务的对话系统的信息理论框架。主动质量管理的好处在于提出一个问题，当它被问到时，这个问题将最大化信息增益。然而，由于其显式计算信息增益的固有性质，主动队列管理有一个局限性时，解决方案的空间是非常大的。为了解决这个问题，我们提出了AQM+，它可以处理大规模的问题，并提出一个与对话的当前上下文更一致的问题。我们评估我们的方法上猜测，一个具有挑战性的面向任务的视觉对话问题，候选类的数量接近10K。我们的实验结果和烧蚀研究表明，AQM+优于国家的最先进的模型的一个显着的利润率与合理的近似。特别是，所提出的AQM+减少了超过60%的错误的对话进行，而比较算法减少了不到6%的错误。基于我们的研究结果，我们认为，AQM+是一个通用的面向任务的对话算法，可以适用于非是或否的反应。
英文摘要：
Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
