Learning Visually Grounded Sentence Representations
Table 5: Spearman ρs correlation on four standard semantic similarity evaluation benchmarks.
['[BOLD] Model', '[BOLD] MEN', '[BOLD] SimLex', '[BOLD] RW', '[BOLD] W353']
Our models contain a projection layer that maps the GloVe word embeddings that they receive as inputs to a different embedding space. There has been a lot of interest in grounded word representations in recent years, so it is interesting to examine what kind of word representations our models learn. We omit Cap2Cap for reasons of space (it performs similarly to Cap2Both). , SimLex-999 Hill et al. et al.