视觉对话模型能否进行计分？探究对话表征如何增量编码共享知识
Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue Representations Incrementally Encode Shared Knowledge
AI总结：
本文提出基于理论的评估方法，探究视觉对话模型在VisDial上预训练后能否增量构建共享知识计分表征，发现区分共享与私有陈述的能力中等但增量一致性不足。
摘要：
认知上合理的视觉对话模型应在对话语境中维护共享既定事实的心理计分板。我们提出一种基于理论的评估方法，用于研究在VisDial数据集上预训练的模型在多大程度上增量构建能恰当进行计分的表征。我们的结论是，在分析的各模型中，沿对话区分共享与私有陈述的能力中等程度存在，但并非总是增量一致的，这可能部分归因于原始任务中对接地交互的需求有限。
英文摘要：
Cognitively plausible visual dialogue models should keep a mental scoreboard of shared established facts in the dialogue context. We propose a theory-based evaluation method for investigating to what degree models pretrained on the VisDial dataset incrementally build representations that appropriately do scorekeeping. Our conclusion is that the ability to make the distinction between shared and privately known statements along the dialogue is moderately present in the analysed models, but not always incrementally consistent, which may partially be due to the limited need for grounding interactions in the original task.
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。
