Learning Grounded Meaning Representations with Autoencoders

In this paper we address the problem of grounding distributional representations of lexical meaning. We introduce a new model which uses stacked autoencoders to learn higher-level embeddings from tex-tual and visual input. The two modali-ties are encoded as vectors of attributes and are obtained automatically from text and images, respectively. We evaluate our model on its ability to simulate similar-ity judgments and concept categorization. On both tasks, our approach outperforms baselines and related models.