Nondeterministic functional transducer inference algorithm

TL;DR

This paper introduces a new algorithm for inferring nondeterministic functional transducers, generalizing existing algorithms like RPNI and OSTIA, and addresses learning from positive and negative examples.

Contribution

It presents a novel inference algorithm for nondeterministic functional transducers that extends and unifies previous methods such as RPNI and OSTIA.

Findings

The algorithm effectively infers nondeterministic functional transducers.

It generalizes RPNI and OSTIA algorithms.

Learning from negative examples is equivalent to learning from positive-only data for these transducers.

Abstract

The purpose of this paper is to present an algorithm for inferring nondeterministic functional transducers. It has a lot in common with other well known algorithms such has RPNI and OSTIA. Indeed we will argue that this algorithm is a generalisation of both of them. Functional transducers are all those nondeterministic transducers whose regular relation is a function. Epsilon transitions as well as subsequential output can be erased for such machines, with the exception of output for empty string being lost. Learning partial functional transducers from negative examples is equivalent to learning total from positive-only data.