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Natural language processing (NLP) can be done using either top-down (theorydriven) and bottom-up (data driven) approaches, which we call mechanistic andphenomenological respectively. The approaches are frequently considered tostand in opposition to each other. Examining some recent approaches in deeplearning we argue that deep neural networks incorporate both perspectives and,furthermore, that leveraging this aspect of deep learning may help in solvingcomplex problems within language technology, such as modelling language andperception in the domain of spatial cognition.

Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing