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.

本研究旨在通过建立使用预训练语言模型的（心理）语言学特征的透明度较高的文本简化系统来提高可解释性和可控性，并利用提前设定的十种属性，拓展一种 Seq2Seq TS 模型，使其能够明确控制多个属性。实验证明我们的方法在解释复杂度预测方面表现优异，并且通过明确地将 Seq2Seq 模型的条件限制于十个属性，其性能在领域内外环境中均有显著提高。