Functorial Language Models
Computation and Language
2021-03-29 v1 Category Theory
Abstract
We introduce functorial language models: a principled way to compute probability distributions over word sequences given a monoidal functor from grammar to meaning. This yields a method for training categorical compositional distributional (DisCoCat) models on raw text data. We provide a proof-of-concept implementation in DisCoPy, the Python toolbox for monoidal categories.
Cite
@article{arxiv.2103.14411,
title = {Functorial Language Models},
author = {Alexis Toumi and Alex Koziell-Pipe},
journal= {arXiv preprint arXiv:2103.14411},
year = {2021}
}
Comments
Submitted to SemSpace 2021