IMPLEMENTING PERCEPTUAL SEMANTICS IN TYPE THEORY WITH RECORDS (TTR)

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Type Theory with Records (TTR) provides accounts of a wide range of semantic and linguistic phenomena in a single framework. This work proposes a TTR model of perception and language. Utilizing PyTTR, a Python implementation of TTR, the model is then implemented as an executable script. Over pure Python programming, TTR provides a transparent formal specification. The implementation is evaluated in a basic visual question answering (VQA) use case scenario. The results show that an implementation of a TTR model can account for multi-modal knowledge representation and work in a VQA setting.

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visual question answering, artificial intelligence, type theory, image recognition, perceptual semantics, spatial relations

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