  We investigate grounded language learning through real-world data, by
modelling a teacher-learner dynamics through the natural interactions occurring
between users and search engines; in particular, we explore the emergence of
semantic generalization from unsupervised dense representations outside of
synthetic environments. A grounding domain, a denotation function and a
composition function are learned from user data only. We show how the resulting
semantics for noun phrases exhibits compositional properties while being fully
learnable without any explicit labelling. We benchmark our grounded semantics
on compositionality and zero-shot inference tasks, and we show that it provides
better results and better generalizations than SOTA non-grounded models, such
as word2vec and BERT.
