  Pre-trained language models (PLMs) have shown remarkable successes in
acquiring a wide range of linguistic knowledge, relying solely on
self-supervised training on text streams. Nevertheless, the effectiveness of
this language-agnostic approach has been frequently questioned for its
sub-optimal performance when applied to morphologically-rich languages (MRLs).
We investigate the hypothesis that incorporating explicit morphological
knowledge in the pre-training phase can improve the performance of PLMs for
MRLs. We propose various morphologically driven tokenization methods enabling
the model to leverage morphological cues beyond raw text. We pre-train multiple
language models utilizing the different methods and evaluate them on Hebrew, a
language with complex and highly ambiguous morphology. Our experiments show
that morphologically driven tokenization demonstrates improved results compared
to a standard language-agnostic tokenization, on a benchmark of both semantic
and morphologic tasks. These findings suggest that incorporating morphological
knowledge holds the potential for further improving PLMs for morphologically
rich languages.
