Compositionality for Recursive Neural Networks

Martha Lewis

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arXiv · 1901.10723

Compositionality for Recursive Neural Networks

Martha Lewis

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Abstract

Modelling compositionality has been a longstanding area of research in the field of vector space semantics. The categorical approach to compositionality maps grammar onto vector spaces in a principled way, but comes under fire for requiring the formation of very high-dimensional matrices and tensors, and therefore being computationally infeasible. In this paper I show how a linear simplification of recursive neural tensor network models can be mapped directly onto the categorical approach, giving a way of computing the required matrices and tensors. This mapping suggests a number of lines of research for both categorical compositional vector space models of meaning and for recursive neural network models of compositionality.

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Martha Lewis. 2019-01-30. Compositionality for Recursive Neural Networks. https://arxiv.org/abs/1901.10723

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