  In this paper, we focus on the challenge of learning controllable text
simplifications in unsupervised settings. While this problem has been
previously discussed for supervised learning algorithms, the literature on the
analogies in unsupervised methods is scarse. We propose two unsupervised
mechanisms for controlling the output complexity of the generated texts,
namely, back translation with control tokens (a learning-based approach) and
simplicity-aware beam search (decoding-based approach). We show that by nudging
a back-translation algorithm to understand the relative simplicity of a text in
comparison to its noisy translation, the algorithm self-supervises itself to
produce the output of the desired complexity. This approach achieves
competitive performance on well-established benchmarks: SARI score of 46.88%
and FKGL of 3.65% on the Newsela dataset.
