  State-of-the-art text simplification (TS) systems adopt end-to-end neural
network models to directly generate the simplified version of the input text,
and usually function as a blackbox. Moreover, TS is usually treated as an
all-purpose generic task under the assumption of homogeneity, where the same
simplification is suitable for all. In recent years, however, there has been
increasing recognition of the need to adapt the simplification techniques to
the specific needs of different target groups. In this work, we aim to advance
current research on explainable and controllable TS in two ways: First,
building on recently proposed work to increase the transparency of TS systems,
we use a large set of (psycho-)linguistic features in combination with
pre-trained language models to improve explainable complexity prediction.
Second, based on the results of this preliminary task, we extend a
state-of-the-art Seq2Seq TS model, ACCESS, to enable explicit control of ten
attributes. The results of experiments show (1) that our approach improves the
performance of state-of-the-art models for predicting explainable complexity
and (2) that explicitly conditioning the Seq2Seq model on ten attributes leads
to a significant improvement in performance in both within-domain and
out-of-domain settings.
