  We introduce a variety of models, trained on a supervised image captioning
corpus to predict the image features for a given caption, to perform sentence
representation grounding. We train a grounded sentence encoder that achieves
good performance on COCO caption and image retrieval and subsequently show that
this encoder can successfully be transferred to various NLP tasks, with
improved performance over text-only models. Lastly, we analyze the contribution
of grounding, and show that word embeddings learned by this system outperform
non-grounded ones.
