论文标题
通过视觉数据学习功能分布语义
Learning Functional Distributional Semantics with Visual Data
论文作者
论文摘要
功能分布语义是最近提出的学习分布语义语义的框架，可提供语言解释性。它将单词作为二进制分类器而不是数值向量的含义建模。在这项工作中，我们提出了一种使用接地视觉数据训练功能分布语义模型的方法。我们在视觉基因组数据集上训练它，该数据集比大型文本语料库更接近人类语言获取的数据类型。在四个外部评估数据集上，我们的模型的表现优于先前从视觉基因组学习语义的工作。
Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. It models the meaning of a word as a binary classifier rather than a numerical vector. In this work, we propose a method to train a Functional Distributional Semantics model with grounded visual data. We train it on the Visual Genome dataset, which is closer to the kind of data encountered in human language acquisition than a large text corpus. On four external evaluation datasets, our model outperforms previous work on learning semantics from Visual Genome.
