  We present the Visually Grounded Neural Syntax Learner (VG-NSL), an approach
for learning syntactic representations and structures without any explicit
supervision. The model learns by looking at natural images and reading paired
captions. VG-NSL generates constituency parse trees of texts, recursively
composes representations for constituents, and matches them with images. We
define concreteness of constituents by their matching scores with images, and
use it to guide the parsing of text. Experiments on the MSCOCO data set show
that VG-NSL outperforms various unsupervised parsing approaches that do not use
visual grounding, in terms of F1 scores against gold parse trees. We find that
VGNSL is much more stable with respect to the choice of random initialization
and the amount of training data. We also find that the concreteness acquired by
VG-NSL correlates well with a similar measure defined by linguists. Finally, we
also apply VG-NSL to multiple languages in the Multi30K data set, showing that
our model consistently outperforms prior unsupervised approaches.
