Multi-modal dialog modeling is of growing interest. In this work, we propose
frameworks to resolve a specific case of multi-modal dialog generation that
better mimics multi-modal dialog generation in the real world, where each
dialog turn is associated with the visual context in which it takes place.
Specifically, we propose to model the mutual dependency between text-visual
features, where the model not only needs to learn the probability of generating
the next dialog utterance given preceding dialog utterances and visual
contexts, but also the probability of predicting the visual features in which a
dialog utterance takes place, leading the generated dialog utterance specific
to the visual context. We observe significant performance boosts over vanilla
models when the mutual dependency between text and visual features is modeled.
Code is available at this https URL

该论文提出了一种解决多模态对话生成的方法，可以更好地模拟真实环境中的多模态对话。通过建模文本 - 视觉特征之间的相互依赖，并研究生成与视觉背景相关的对话，大幅提升了模型性能。