Visually Grounded Neural Syntax Acquisition
Table 2: Agreement between our concreteness estimates and existing models or labels, evaluated via the Pearson correlation coefficient computed over the most frequent 100 words in the MSCOCO test set, averaged over 5 runs with different random initialization.
['[BOLD] Model/method', 'VG-NSL', '(+HI)']
Here, we evaluate the consistency between word-level concreteness estimates induced by VG-NSL and those produced by other methods turney2011literal; brysbaert2014concreteness; hessel2018quantifying. For any word, of which the representation is z, we estimate its concreteness by taking the average of concrete(z,v(i)), across all associated images v(i). The high correlation between VG-NSL and the concreteness scores produced by \newciteturney2011literal and \newcitebrysbaert2014concreteness supports the argument that the linguistic concept of concreteness can be acquired in an unsupervised way. Our model also achieves a high correlation with hessel2018quantifying, which also estimates word concreteness based on visual-domain information.