Techniques are described herein for training machine learning models to simplify (e.g., paraphrase) complex textual content by ensuring that the machine learning models jointly learn both semantic alignment and notions of simplicity. In various embodiments, an input textual segment having multiple tokens and being associated with a first measure of simplicity may be applied as input across a trained machine learning model to generate an output textual segment. The output textual segment may be is semantically aligned with the input textual segment and associated with a second measure of simplicity that is greater than the first measure of simplicity (e.g., a paraphrase thereof). The trained machine learning model may include an encoder portion and a decoder portion, as well as control layer(s) trained to maximize the second measure of simplicity by replacing token(s) of the input textual segment with replacement token(s).