  How does the input segmentation of pretrained language models (PLMs) affect
their generalization capabilities? We present the first study investigating
this question, taking BERT as the example PLM and focusing on the semantic
representations of derivationally complex words. We show that PLMs can be
interpreted as serial dual-route models, i.e., the meanings of complex words
are either stored or else need to be computed from the subwords, which implies
that maximally meaningful input tokens should allow for the best generalization
on new words. This hypothesis is confirmed by a series of semantic probing
tasks on which derivational segmentation consistently outperforms BERT's
WordPiece segmentation by a large margin. Our results suggest that the
generalization capabilities of PLMs could be further improved if a
morphologically-informed vocabulary of input tokens were used.
