  This paper introduces mFST, a new Python library for working with
Finite-State Machines based on OpenFST. mFST is a thin wrapper for OpenFST and
exposes all of OpenFST's methods for manipulating FSTs. Additionally, mFST is
the only Python wrapper for OpenFST that exposes OpenFST's ability to define a
custom semirings. This makes mFST ideal for developing models that involve
learning the weights on a FST or creating neuralized FSTs. mFST has been
designed to be easy to get started with and has been previously used in
homework assignments for a NLP class as well in projects for integrating FSTs
and neural networks. In this paper, we exhibit mFST API and how to use mFST to
build a simple neuralized FST with PyTorch.
