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Full-Text Articles in Physical Sciences and Mathematics
What Is Not Where: The Challenge Of Integrating Spatial Representations Into Deep Learning Architectures, John D. Kelleher, Simon Dobnik
What Is Not Where: The Challenge Of Integrating Spatial Representations Into Deep Learning Architectures, John D. Kelleher, Simon Dobnik
Books/Book chapters
This paper examines to what degree current deep learning architectures for image caption generation capture spatial lan- guage. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the cap- tions generated by these systems are the output of a language model conditioned on the output of an object detector that cannot capture fine-grained location information. Although language models provide useful knowledge for image captions, we argue that deep learning image captioning architectures should also model geometric …
Modular Mechanistic Networks: On Bridging Mechanistic And Phenomenological Models With Deep Neural Networks In Natural Language Processing, Simon Dobnik, John D. Kelleher
Modular Mechanistic Networks: On Bridging Mechanistic And Phenomenological Models With Deep Neural Networks In Natural Language Processing, Simon Dobnik, John D. Kelleher
Books/Book chapters
Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition.