  We study the problem of learning a single occurrence regular expression with
interleaving (SOIRE) from a set of text strings possibly with noise. SOIRE
fully supports interleaving and covers a large portion of regular expressions
used in practice. Learning SOIREs is challenging because it requires heavy
computation and text strings usually contain noise in practice. Most of the
previous studies only learn restricted SOIREs and are not robust on noisy data.
To tackle these issues, we propose a noise-tolerant differentiable learning
approach SOIREDL for SOIRE. We design a neural network to simulate SOIRE
matching and theoretically prove that certain assignments of the set of
parameters learnt by the neural network, called faithful encodings, are
one-to-one corresponding to SOIREs for a bounded size. Based on this
correspondence, we interpret the target SOIRE from an assignment of the set of
parameters of the neural network by exploring the nearest faithful encodings.
Experimental results show that SOIREDL outperforms the state-of-the-art
approaches, especially on noisy data.
