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.

研究通过现实世界数据进行基于场景语言学习，探索在用户与搜索引擎之间自然互动中建模教师 - 学生动态学习的出现、语义的普遍性，学习场景、表示和组合的功能，并表明该方法在组合性和零 - shot 推理任务方面提供了比 SOTA 的非基于场景的模型更好的结果和泛化能力。