  In this paper, we explore the impact of augmenting pre-trained
Encoder-Decoder models, specifically T5, with linguistic knowledge for the
prediction of a target task. In particular, we investigate whether fine-tuning
a T5 model on an intermediate task that predicts structural linguistic
properties of sentences modifies its performance in the target task of
predicting sentence-level complexity. Our study encompasses diverse experiments
conducted on Italian and English datasets, employing both monolingual and
multilingual T5 models at various sizes. Results obtained for both languages
and in cross-lingual configurations show that linguistically motivated
intermediate fine-tuning has generally a positive impact on target task
performance, especially when applied to smaller models and in scenarios with
limited data availability.
