Understanding language goes hand in hand with the ability to integrate
complex contextual information obtained via perception. In this work, we
present a novel task for grounded language understanding: disambiguating a
sentence given a visual scene which depicts one of the possible interpretations
of that sentence. To this end, we introduce a new multimodal corpus containing
ambiguous sentences, representing a wide range of syntactic, semantic and
discourse ambiguities, coupled with videos that visualize the different
interpretations for each sentence. We address this task by extending a vision
model which determines if a sentence is depicted by a video. We demonstrate how
such a model can be adjusted to recognize different interpretations of the same
underlying sentence, allowing to disambiguate sentences in a unified fashion
across the different ambiguity types.

通过感知获得的复杂上下文信息与理解语言相辅相成。本文提出了一项新颖的基于场景的语言理解任务：在给定描绘该句子可能解释之一的视觉场景的情况下，将其消除歧义。为此，我们介绍了一个包含多义句子的新的多模态语料库，表示广泛的句法、语义和语篇歧义，并与可视化不同解释的视频相耦合。我们通过扩展决定句子是否被视频描绘的视觉模型来解决这个任务。我们演示了如何调整这样的模型以识别同一基础句子的不同解释，从而允许以统一的方式消除不同歧义类型的句子。